From 02054da187afd7ae1758a4cf176fb9cc98ccce12 Mon Sep 17 00:00:00 2001
From: Neta Zmora <31280975+nzmora@users.noreply.github.com>
Date: Tue, 6 Aug 2019 23:20:14 +0300
Subject: [PATCH] AMC and other refactoring - large merge (#339)
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*An implementation of AMC (the previous implementation
code has moved to a new location under
/distiller/examples/auto_compression/amc. AMC is aligned
with the ‘master’ branch of Coach.
*compress_classifier.py is refactored. The base code moved
to /distiller/apputils/image_classifier.py. Further refactoring
will follow.
We want to provide a simple and small API to the basic features of
a classifier-compression application.
This will help applications that want to use the make features of a
classifier-compression application, without the standard training
regiment.
AMC is one example of a stand-alone application that needs to leverage
the capabilities of a classifier-compression application, but is currently
coupled to `compress_classifier.py`.
`multi-finetune.py` is another example.
* ranked_structures_pruner.py:
** Added support for grouping channels/filters
Sometimes we want to prune a group of structures: e.g. groups of
8-channels. This feature does not force the groups to be adjacent,
so it is more like a set of structures. E.g. in the case of pruning
channels from a 64-channels convolution, grouped by 8 channels, we
will prune exactly one of 0/8/16/24/32/40/48/56 channels. I.e.
always a multiple of 8-channels, excluding the set of all 64 channels.
** Added FMReconstructionChannelPruner – this is channel
pruning using L1-magnitude to rank and select channels to
remove, and feature-map reconstruction to improve the
resilience to the pruning.
* Added a script to run multiple instances of an
experiment, in different processes:
examples/classifier_compression/multi-run.py
* Set the seed value even when not specified by the command-line
arguments, so that we can try and recreate the session.
* Added pruning ranking noise -
Ranking noise introduces Gaussian noise when ranking channels/filters
using Lp-norm. The noise is introduced using the epsilon-greedy
methodology, where ranking using exact Lp-norm is considered greedy.
* Added configurable rounding of pruning level: choose whether to
Round up/down when rounding the number of structures to prune
(rounding is always to an integer).
---
README.md | 5 +-
distiller/apputils/image_classifier.py | 837 ++++
distiller/data_loggers/tbbackend.py | 3 +
distiller/pruning/__init__.py | 3 +-
distiller/pruning/ranked_structures_pruner.py | 399 +-
distiller/summary_graph.py | 6 +-
distiller/thinning.py | 16 +-
examples/auto_compression/README.md | 6 +
.../ddpg => auto_compression}/__init__.py | 0
examples/auto_compression/amc/README.md | 38 +
examples/auto_compression/amc/__init__.py | 0
.../amc}/adc_vgg16_cifar_finetuning.yaml | 0
examples/auto_compression/amc/amc.py | 216 +
.../amc/amc_args.py} | 37 +-
.../amc/auto_compression_channels.yaml | 100 +
.../amc/auto_compression_filters.yaml | 58 +
examples/auto_compression/amc/environment.py | 823 ++++
.../amc/imgs/amc_worksheet.png | Bin 0 -> 445213 bytes
.../amc/jupyter/amc_plain20.ipynb | 626 +++
.../amc/jupyter/amc_random.ipynb | 453 +++
.../amc/jupyter/amc_resnet20.ipynb | 245 ++
.../amc/jupyter/amc_worksheet.ipynb | 701 ++++
.../amc/jupyter/amc_worksheet_jupyter.py | 286 ++
.../amc/jupyter/auto_compression_jupyter.py | 328 ++
examples/auto_compression/amc/logging.conf | 60 +
.../auto_compression/amc/parallel-finetune.py | 243 ++
examples/auto_compression/amc/rewards.py | 111 +
.../auto_compression/amc/rl_libs/__init__.py | 0
.../amc/rl_libs/coach/__init__.py | 0
.../amc/rl_libs/coach/coach_if.py | 77 +
.../rl_libs/coach}/presets/ADC_ClippedPPO.py | 1 +
.../amc/rl_libs/coach}/presets/ADC_DDPG.py | 60 +-
.../amc/rl_libs/coach}/presets/ADC_PPO.py | 14 -
.../amc/rl_libs/coach/presets/ADC_TD3.py | 54 +
.../amc/rl_libs/coach/presets/__init__.py | 0
.../amc/rl_libs/private/__init__.py | 0
.../amc/rl_libs/private/private_if.py | 54 +
.../amc/rl_libs/random/__init__.py | 0
.../amc/rl_libs/random/random_agent.py | 56 +
.../amc/rl_libs/random/random_if.py | 29 +
.../amc/rl_libs/spinningup/__init__.py | 0
.../amc/rl_libs/spinningup}/ddpg/README | 0
.../amc/rl_libs/spinningup/ddpg/__init__.py | 0
.../amc/rl_libs/spinningup}/ddpg/core.py | 21 +-
.../amc/rl_libs/spinningup}/ddpg/ddpg.py | 5 +-
.../amc/rl_libs/spinningup/installation.md | 29 +
.../amc/rl_libs/spinningup/spinningup_if.py | 56 +
.../auto_compression/amc/utils/__init__.py | 0
.../auto_compression/amc/utils/ac_loggers.py | 48 +
.../amc/utils/data_dependencies.py | 70 +
.../amc/utils/features_collector.py | 80 +
.../plain20_cifar_baseline_training.yaml | 0
.../auto_compression/plain20_fine_tune.yaml | 23 +
.../vgg16_cifar_baseline_training.yaml | 0
examples/automated_deep_compression/ADC.py | 913 -----
.../automated_deep_compression/__init__.py | 5 -
.../adc_random_env.py | 66 -
.../amc-results.ipynb | 838 ----
.../automated_deep_compression/fine_tune.yaml | 15 -
.../ppo-amc-results.ipynb | 577 ---
.../accuracy-guaranteed_clipped-ppo.amc.csv | 3153 ---------------
.../macs_constrained_clipped-ppo.amc.csv | 3550 -----------------
.../AMC-Plain20___2018.12.29-033839/amc.csv | 461 ---
...rl_graph.main_level.main_level.agent_0.csv | 401 --
.../events.out.tfevents.1546047525.cvds-18 | Bin 927229 -> 0 bytes
.../compress_classifier.py | 721 +---
examples/classifier_compression/multi-run.py | 66 +
examples/classifier_compression/parser.py | 116 +-
.../ssl/resnet20_cifar_baseline_training.yaml | 22 +
examples/util_scripts/multi-run.py | 49 +
tests/full_flow_tests.py | 2 +-
tests/test_pruning.py | 14 +-
72 files changed, 6357 insertions(+), 10889 deletions(-)
create mode 100755 distiller/apputils/image_classifier.py
create mode 100755 examples/auto_compression/README.md
rename examples/{automated_deep_compression/ddpg => auto_compression}/__init__.py (100%)
mode change 100644 => 100755
create mode 100755 examples/auto_compression/amc/README.md
create mode 100644 examples/auto_compression/amc/__init__.py
rename examples/{automated_deep_compression => auto_compression/amc}/adc_vgg16_cifar_finetuning.yaml (100%)
create mode 100755 examples/auto_compression/amc/amc.py
rename examples/{automated_deep_compression/automl_args.py => auto_compression/amc/amc_args.py} (52%)
create mode 100755 examples/auto_compression/amc/auto_compression_channels.yaml
create mode 100755 examples/auto_compression/amc/auto_compression_filters.yaml
create mode 100755 examples/auto_compression/amc/environment.py
create mode 100644 examples/auto_compression/amc/imgs/amc_worksheet.png
create mode 100644 examples/auto_compression/amc/jupyter/amc_plain20.ipynb
create mode 100644 examples/auto_compression/amc/jupyter/amc_random.ipynb
create mode 100644 examples/auto_compression/amc/jupyter/amc_resnet20.ipynb
create mode 100644 examples/auto_compression/amc/jupyter/amc_worksheet.ipynb
create mode 100644 examples/auto_compression/amc/jupyter/amc_worksheet_jupyter.py
create mode 100755 examples/auto_compression/amc/jupyter/auto_compression_jupyter.py
create mode 100755 examples/auto_compression/amc/logging.conf
create mode 100755 examples/auto_compression/amc/parallel-finetune.py
create mode 100755 examples/auto_compression/amc/rewards.py
create mode 100644 examples/auto_compression/amc/rl_libs/__init__.py
create mode 100644 examples/auto_compression/amc/rl_libs/coach/__init__.py
create mode 100755 examples/auto_compression/amc/rl_libs/coach/coach_if.py
rename examples/{automated_deep_compression => auto_compression/amc/rl_libs/coach}/presets/ADC_ClippedPPO.py (98%)
rename examples/{automated_deep_compression => auto_compression/amc/rl_libs/coach}/presets/ADC_DDPG.py (59%)
rename examples/{automated_deep_compression => auto_compression/amc/rl_libs/coach}/presets/ADC_PPO.py (78%)
mode change 100644 => 100755
create mode 100755 examples/auto_compression/amc/rl_libs/coach/presets/ADC_TD3.py
create mode 100644 examples/auto_compression/amc/rl_libs/coach/presets/__init__.py
create mode 100644 examples/auto_compression/amc/rl_libs/private/__init__.py
create mode 100755 examples/auto_compression/amc/rl_libs/private/private_if.py
create mode 100644 examples/auto_compression/amc/rl_libs/random/__init__.py
create mode 100755 examples/auto_compression/amc/rl_libs/random/random_agent.py
create mode 100755 examples/auto_compression/amc/rl_libs/random/random_if.py
create mode 100644 examples/auto_compression/amc/rl_libs/spinningup/__init__.py
rename examples/{automated_deep_compression => auto_compression/amc/rl_libs/spinningup}/ddpg/README (100%)
create mode 100644 examples/auto_compression/amc/rl_libs/spinningup/ddpg/__init__.py
rename examples/{automated_deep_compression => auto_compression/amc/rl_libs/spinningup}/ddpg/core.py (62%)
rename examples/{automated_deep_compression => auto_compression/amc/rl_libs/spinningup}/ddpg/ddpg.py (99%)
create mode 100755 examples/auto_compression/amc/rl_libs/spinningup/installation.md
create mode 100755 examples/auto_compression/amc/rl_libs/spinningup/spinningup_if.py
create mode 100644 examples/auto_compression/amc/utils/__init__.py
create mode 100755 examples/auto_compression/amc/utils/ac_loggers.py
create mode 100755 examples/auto_compression/amc/utils/data_dependencies.py
create mode 100755 examples/auto_compression/amc/utils/features_collector.py
rename examples/{automated_deep_compression => auto_compression}/plain20_cifar_baseline_training.yaml (100%)
create mode 100755 examples/auto_compression/plain20_fine_tune.yaml
rename examples/{automated_deep_compression => auto_compression}/vgg16_cifar_baseline_training.yaml (100%)
delete mode 100755 examples/automated_deep_compression/ADC.py
delete mode 100755 examples/automated_deep_compression/__init__.py
delete mode 100755 examples/automated_deep_compression/adc_random_env.py
delete mode 100644 examples/automated_deep_compression/amc-results.ipynb
delete mode 100755 examples/automated_deep_compression/fine_tune.yaml
delete mode 100644 examples/automated_deep_compression/ppo-amc-results.ipynb
delete mode 100644 examples/automated_deep_compression/sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv
delete mode 100644 examples/automated_deep_compression/sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv
delete mode 100644 examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv
delete mode 100644 examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/coach/worker_0.simple_rl_graph.main_level.main_level.agent_0.csv
delete mode 100644 examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/events.out.tfevents.1546047525.cvds-18
create mode 100644 examples/classifier_compression/multi-run.py
create mode 100755 examples/util_scripts/multi-run.py
diff --git a/README.md b/README.md
index 75a2a77..60f97f0 100755
--- a/README.md
+++ b/README.md
@@ -132,7 +132,8 @@ Beware.
- [Disclaimer](#disclaimer)
## Highlighted features
-
+* **Automatic Compression**
+ - [Automated Model Compression](https://github.com/NervanaSystems/distiller/blob/master/examples/auto_compression/amc) (AMC)
* **Weight pruning**
- Element-wise pruning using magnitude thresholding, sensitivity thresholding, target sparsity level, and activation statistics
* **Structured pruning**
@@ -162,7 +163,7 @@ Beware.
* **Knowledge distillation**
- Training with [knowledge distillation](https://nervanasystems.github.io/distiller/knowledge_distillation.html), in conjunction with the other available pruning / regularization / quantization methods.
* **Conditional computation**
- - Sample implementation of Early Exit, with more to come
+ - Sample implementation of Early Exit
* Export statistics summaries using Pandas dataframes, which makes it easy to slice, query, display and graph the data.
* A set of [Jupyter notebooks](https://nervanasystems.github.io/distiller/jupyter/index.html) to plan experiments and analyze compression results. The graphs and visualizations you see on this page originate from the included Jupyter notebooks.
+ Take a look at [this notebook](https://github.com/NervanaSystems/distiller/blob/master/jupyter/alexnet_insights.ipynb), which compares visual aspects of dense and sparse Alexnet models.
diff --git a/distiller/apputils/image_classifier.py b/distiller/apputils/image_classifier.py
new file mode 100755
index 0000000..ddfb0ed
--- /dev/null
+++ b/distiller/apputils/image_classifier.py
@@ -0,0 +1,837 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import math
+import time
+import os
+import logging
+from collections import OrderedDict
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+import torch.backends.cudnn as cudnn
+import torch.optim
+import torch.utils.data
+import torchnet.meter as tnt
+import distiller
+import distiller.apputils as apputils
+from distiller.data_loggers import *
+import distiller.quantization as quantization
+import distiller.models as models
+from distiller.models import create_model
+import parser
+import partial
+import operator
+import argparse
+from distiller.utils import float_range_argparse_checker as float_range
+
+# Logger handle
+msglogger = logging.getLogger()
+
+
+class ClassifierCompressor(object):
+ """Base class for applications that want to compress image classifiers.
+
+ This class performs boiler-plate code used in image-classifiers compression:
+ - Command-line arguments handling
+ - Logger configuration
+ - Data loading
+ - Checkpoint handling
+ - Classifier training, verification and testing
+ """
+ def __init__(self, args, script_dir):
+ self.args = args
+ _override_args(args)
+ self.logdir = _init_logger(args, script_dir)
+ _config_determinism(args)
+ _config_compute_device(args)
+
+ # Create a couple of logging backends. TensorBoardLogger writes log files in a format
+ # that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
+ self.tflogger = TensorBoardLogger(msglogger.logdir)
+ self.pylogger = PythonLogger(msglogger)
+ (self.model, self.compression_scheduler, self.optimizer,
+ self.start_epoch, self.ending_epoch) = _init_learner(args)
+
+ # Define loss function (criterion)
+ self.criterion = nn.CrossEntropyLoss().to(args.device)
+ self.train_loader, self.val_loader, self.test_loader = (None, None, None)
+ self.activations_collectors = create_activation_stats_collectors(self.model, *args.activation_stats)
+
+ def load_datasets(self):
+ """Load the datasets"""
+ self.train_loader, self.val_loader, self.test_loader = load_data(self.args)
+
+ def train_one_epoch(self, epoch, verbose=True):
+ if self.train_loader is None:
+ self.load_datasets()
+
+ # Train for one epoch
+ with collectors_context(self.activations_collectors["train"]) as collectors:
+ top1, top5, loss = train(self.train_loader, self.model, self.criterion, self.optimizer,
+ epoch, self.compression_scheduler,
+ loggers=[self.tflogger, self.pylogger], args=self.args)
+ if verbose:
+ distiller.log_weights_sparsity(self.model, epoch, [self.tflogger, self.pylogger])
+ distiller.log_activation_statsitics(epoch, "train", loggers=[self.tflogger],
+ collector=collectors["sparsity"])
+ if self.args.masks_sparsity:
+ msglogger.info(distiller.masks_sparsity_tbl_summary(self.model,
+ self.compression_scheduler))
+ return top1, top5, loss
+
+ def train_validate_with_scheduling(self, epoch, validate=True, verbose=True):
+ if self.compression_scheduler:
+ self.compression_scheduler.on_epoch_begin(epoch)
+
+ top1, top5, loss = self.train_one_epoch(epoch, verbose)
+ if validate:
+ top1, top5, loss = self.validate_one_epoch(epoch, verbose)
+
+ if self.compression_scheduler:
+ self.compression_scheduler.on_epoch_end(epoch, self.optimizer,
+ metrics={'min': loss, 'max': top1})
+ return top1, top5, loss
+
+ def validate_one_epoch(self, epoch, verbose=True):
+ # evaluate on validation set
+ with collectors_context(self.activations_collectors["valid"]) as collectors:
+ top1, top5, vloss = validate(self.val_loader, self.model, self.criterion,
+ [self.pylogger], self.args, epoch)
+ distiller.log_activation_statsitics(epoch, "valid", loggers=[self.tflogger],
+ collector=collectors["sparsity"])
+ save_collectors_data(collectors, msglogger.logdir)
+
+ if verbose:
+ stats = ('Performance/Validation/',
+ OrderedDict([('Loss', vloss),
+ ('Top1', top1),
+ ('Top5', top5)]))
+ distiller.log_training_progress(stats, None, epoch, steps_completed=0, total_steps=1, log_freq=1,
+ loggers=[self.tflogger])
+
+ return top1, top5, vloss
+
+ def _finalize_epoch(self, epoch, perf_scores_history, top1, top5):
+ # Update the list of top scores achieved so far, and save the checkpoint
+ update_training_scores_history(perf_scores_history, self.model,
+ top1, top5, epoch, self.args.num_best_scores)
+ is_best = epoch == perf_scores_history[0].epoch
+ checkpoint_extras = {'current_top1': top1,
+ 'best_top1': perf_scores_history[0].top1,
+ 'best_epoch': perf_scores_history[0].epoch}
+ apputils.save_checkpoint(epoch, self.args.arch, self.model, optimizer=self.optimizer,
+ scheduler=self.compression_scheduler, extras=checkpoint_extras,
+ is_best=is_best, name=self.args.name, dir=msglogger.logdir)
+
+
+ def run_training_loop(self):
+ """Run the main training loop with compression.
+
+ For each epoch:
+ train_one_epoch
+ validate_one_epoch
+ finalize_epoch
+ """
+ if self.train_loader is None:
+ # Load the datasets lazily
+ self.load_datasets()
+
+ perf_scores_history = []
+ for epoch in range(self.start_epoch, self.ending_epoch):
+ msglogger.info('\n')
+ top1, top5, loss = self.train_validate_with_scheduling(epoch)
+ self._finalize_epoch(epoch, perf_scores_history, top1, top5)
+
+ def validate(self, epoch=-1):
+ return validate(self.val_loader, self.model, self.criterion,
+ [self.tflogger, self.pylogger], self.args, epoch)
+
+ def test(self):
+ return test(self.test_loader, self.model, self.criterion,
+ self.pylogger, self.activations_collectors, args=self.args)
+
+
+def init_classifier_compression_arg_parser():
+ '''Common classifier-compression application command-line arguments.
+ '''
+ SUMMARY_CHOICES = ['sparsity', 'compute', 'model', 'modules', 'png', 'png_w_params']
+
+ parser = argparse.ArgumentParser(description='Distiller image classification model compression')
+ parser.add_argument('data', metavar='DIR', help='path to dataset')
+ parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18', type=lambda s: s.lower(),
+ choices=models.ALL_MODEL_NAMES,
+ help='model architecture: ' +
+ ' | '.join(models.ALL_MODEL_NAMES) +
+ ' (default: resnet18)')
+ parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
+ help='number of data loading workers (default: 4)')
+ parser.add_argument('--epochs', type=int, metavar='N', default=90,
+ help='number of total epochs to run (default: 90')
+ parser.add_argument('-b', '--batch-size', default=256, type=int,
+ metavar='N', help='mini-batch size (default: 256)')
+
+ optimizer_args = parser.add_argument_group('Optimizer arguments')
+ optimizer_args.add_argument('--lr', '--learning-rate', default=0.1,
+ type=float, metavar='LR', help='initial learning rate')
+ optimizer_args.add_argument('--momentum', default=0.9, type=float,
+ metavar='M', help='momentum')
+ optimizer_args.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
+ metavar='W', help='weight decay (default: 1e-4)')
+
+ parser.add_argument('--print-freq', '-p', default=10, type=int,
+ metavar='N', help='print frequency (default: 10)')
+ parser.add_argument('--verbose', '-v', action='store_true', help='Emit debug log messages')
+
+ load_checkpoint_group = parser.add_argument_group('Resuming arguments')
+ load_checkpoint_group_exc = load_checkpoint_group.add_mutually_exclusive_group()
+ # TODO(barrh): args.deprecated_resume is deprecated since v0.3.1
+ load_checkpoint_group_exc.add_argument('--resume', dest='deprecated_resume', default='', type=str,
+ metavar='PATH', help=argparse.SUPPRESS)
+ load_checkpoint_group_exc.add_argument('--resume-from', dest='resumed_checkpoint_path', default='',
+ type=str, metavar='PATH',
+ help='path to latest checkpoint. Use to resume paused training session.')
+ load_checkpoint_group_exc.add_argument('--exp-load-weights-from', dest='load_model_path',
+ default='', type=str, metavar='PATH',
+ help='path to checkpoint to load weights from (excluding other fields) (experimental)')
+ load_checkpoint_group.add_argument('--pretrained', dest='pretrained', action='store_true',
+ help='use pre-trained model')
+ load_checkpoint_group.add_argument('--reset-optimizer', action='store_true',
+ help='Flag to override optimizer if resumed from checkpoint. This will reset epochs count.')
+
+ parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
+ help='evaluate model on test set')
+ parser.add_argument('--activation-stats', '--act-stats', nargs='+', metavar='PHASE', default=list(),
+ help='collect activation statistics on phases: train, valid, and/or test'
+ ' (WARNING: this slows down training)')
+ parser.add_argument('--activation-histograms', '--act-hist',
+ type=distiller.utils.float_range_argparse_checker(exc_min=True),
+ metavar='PORTION_OF_TEST_SET',
+ help='Run the model in evaluation mode on the specified portion of the test dataset and '
+ 'generate activation histograms. NOTE: This slows down evaluation significantly')
+ parser.add_argument('--masks-sparsity', dest='masks_sparsity', action='store_true', default=False,
+ help='print masks sparsity table at end of each epoch')
+ parser.add_argument('--param-hist', dest='log_params_histograms', action='store_true', default=False,
+ help='log the parameter tensors histograms to file '
+ '(WARNING: this can use significant disk space)')
+ parser.add_argument('--summary', type=lambda s: s.lower(), choices=SUMMARY_CHOICES, action='append',
+ help='print a summary of the model, and exit - options: | '.join(SUMMARY_CHOICES))
+ parser.add_argument('--export-onnx', action='store', nargs='?', type=str, const='model.onnx', default=None,
+ help='export model to ONNX format')
+ parser.add_argument('--compress', dest='compress', type=str, nargs='?', action='store',
+ help='configuration file for pruning the model (default is to use hard-coded schedule)')
+ parser.add_argument('--sense', dest='sensitivity', choices=['element', 'filter', 'channel'],
+ type=lambda s: s.lower(), help='test the sensitivity of layers to pruning')
+ parser.add_argument('--sense-range', dest='sensitivity_range', type=float, nargs=3, default=[0.0, 0.95, 0.05],
+ help='an optional parameter for sensitivity testing '
+ 'providing the range of sparsities to test.\n'
+ 'This is equivalent to creating sensitivities = np.arange(start, stop, step)')
+ parser.add_argument('--extras', default=None, type=str,
+ help='file with extra configuration information')
+ parser.add_argument('--deterministic', '--det', action='store_true',
+ help='Ensure deterministic execution for re-producible results.')
+ parser.add_argument('--seed', type=int, default=None,
+ help='seed the PRNG for CPU, CUDA, numpy, and Python')
+ parser.add_argument('--gpus', metavar='DEV_ID', default=None,
+ help='Comma-separated list of GPU device IDs to be used '
+ '(default is to use all available devices)')
+ parser.add_argument('--cpu', action='store_true', default=False,
+ help='Use CPU only. \n'
+ 'Flag not set => uses GPUs according to the --gpus flag value.'
+ 'Flag set => overrides the --gpus flag')
+ parser.add_argument('--name', '-n', metavar='NAME', default=None, help='Experiment name')
+ parser.add_argument('--out-dir', '-o', dest='output_dir', default='logs', help='Path to dump logs and checkpoints')
+ parser.add_argument('--validation-split', '--valid-size', '--vs', dest='validation_split',
+ type=float_range(exc_max=True), default=0.1,
+ help='Portion of training dataset to set aside for validation')
+ parser.add_argument('--effective-train-size', '--etrs', type=float_range(exc_min=True), default=1.,
+ help='Portion of training dataset to be used in each epoch. '
+ 'NOTE: If --validation-split is set, then the value of this argument is applied '
+ 'AFTER the train-validation split according to that argument')
+ parser.add_argument('--effective-valid-size', '--evs', type=float_range(exc_min=True), default=1.,
+ help='Portion of validation dataset to be used in each epoch. '
+ 'NOTE: If --validation-split is set, then the value of this argument is applied '
+ 'AFTER the train-validation split according to that argument')
+ parser.add_argument('--effective-test-size', '--etes', type=float_range(exc_min=True), default=1.,
+ help='Portion of test dataset to be used in each epoch')
+ parser.add_argument('--confusion', dest='display_confusion', default=False, action='store_true',
+ help='Display the confusion matrix')
+ parser.add_argument('--num-best-scores', dest='num_best_scores', default=1, type=int,
+ help='number of best scores to track and report (default: 1)')
+ parser.add_argument('--load-serialized', dest='load_serialized', action='store_true', default=False,
+ help='Load a model without DataParallel wrapping it')
+ parser.add_argument('--thinnify', dest='thinnify', action='store_true', default=False,
+ help='physically remove zero-filters and create a smaller model')
+
+ distiller.quantization.add_post_train_quant_args(parser)
+ return parser
+
+
+def _init_logger(args, script_dir):
+ module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
+ global msglogger
+
+ if not os.path.exists(args.output_dir):
+ os.makedirs(args.output_dir)
+ msglogger = apputils.config_pylogger(os.path.join(script_dir, 'logging.conf'),
+ args.name, args.output_dir, args.verbose)
+
+ # Log various details about the execution environment. It is sometimes useful
+ # to refer to past experiment executions and this information may be useful.
+ apputils.log_execution_env_state(
+ filter(None, [args.compress, args.qe_stats_file]), # remove both None and empty strings
+ msglogger.logdir, gitroot=module_path)
+ msglogger.debug("Distiller: %s", distiller.__version__)
+ return msglogger.logdir
+
+def _config_determinism(args):
+ if args.evaluate:
+ args.deterministic = True
+
+ # Configure some seed (in case we want to reproduce this experiment session)
+ if args.seed is None:
+ if args.deterministic:
+ args.seed = 0
+ else:
+ args.seed = np.random.randint(1, 100000)
+
+ if args.deterministic:
+ distiller.set_deterministic(args.seed) # For experiment reproducability
+ else:
+ distiller.set_seed(args.seed)
+ # Turn on CUDNN benchmark mode for best performance. This is usually "safe" for image
+ # classification models, as the input sizes don't change during the run
+ # See here: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3
+ cudnn.benchmark = True
+ msglogger.info("Random seed: %d", args.seed)
+
+def _config_compute_device(args):
+ if args.cpu or not torch.cuda.is_available():
+ # Set GPU index to -1 if using CPU
+ args.device = 'cpu'
+ args.gpus = -1
+ else:
+ args.device = 'cuda'
+ if args.gpus is not None:
+ try:
+ args.gpus = [int(s) for s in args.gpus.split(',')]
+
+ except ValueError:
+ raise ValueError('ERROR: Argument --gpus must be a comma-separated list of integers only')
+ available_gpus = torch.cuda.device_count()
+ for dev_id in args.gpus:
+ if dev_id >= available_gpus:
+ raise ValueError('ERROR: GPU device ID {0} requested, but only {1} devices available'
+ .format(dev_id, available_gpus))
+ # Set default device in case the first one on the list != 0
+ torch.cuda.set_device(args.gpus[0])
+
+
+def _override_args(args):
+ # Infer the dataset from the model name
+ args.dataset = distiller.apputils.classification_dataset_str_from_arch(args.arch)
+ args.num_classes = distiller.apputils.classification_num_classes(args.dataset)
+
+
+def _init_learner(args):
+ # Create the model
+ model = create_model(args.pretrained, args.dataset, args.arch,
+ parallel=not args.load_serialized, device_ids=args.gpus)
+ compression_scheduler = None
+
+ # TODO(barrh): args.deprecated_resume is deprecated since v0.3.1
+ if args.deprecated_resume:
+ msglogger.warning('The "--resume" flag is deprecated. Please use "--resume-from=YOUR_PATH" instead.')
+ if not args.reset_optimizer:
+ msglogger.warning('If you wish to also reset the optimizer, call with: --reset-optimizer')
+ args.reset_optimizer = True
+ args.resumed_checkpoint_path = args.deprecated_resume
+
+ optimizer = None
+ start_epoch = 0
+ if args.resumed_checkpoint_path:
+ model, compression_scheduler, optimizer, start_epoch = apputils.load_checkpoint(
+ model, args.resumed_checkpoint_path, model_device=args.device)
+ elif args.load_model_path:
+ model = apputils.load_lean_checkpoint(model, args.load_model_path,
+ model_device=args.device)
+ if args.reset_optimizer:
+ start_epoch = 0
+ if optimizer is not None:
+ optimizer = None
+ msglogger.info('\nreset_optimizer flag set: Overriding resumed optimizer and resetting epoch count to 0')
+
+ if optimizer is None:
+ optimizer = torch.optim.SGD(model.parameters(),
+ lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
+ msglogger.debug('Optimizer Type: %s', type(optimizer))
+ msglogger.debug('Optimizer Args: %s', optimizer.defaults)
+
+ if args.compress:
+ # The main use-case for this sample application is CNN compression. Compression
+ # requires a compression schedule configuration file in YAML.
+ compression_scheduler = distiller.file_config(model, optimizer, args.compress, compression_scheduler,
+ (start_epoch-1) if args.resumed_checkpoint_path else None)
+ # Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
+ model.to(args.device)
+ elif compression_scheduler is None:
+ compression_scheduler = distiller.CompressionScheduler(model)
+
+ ending_epoch = args.epochs
+ if start_epoch >= ending_epoch:
+ msglogger.error(
+ 'epoch count is too low, starting epoch is {} but total epochs set to {}'.format(
+ start_epoch, ending_epoch))
+ raise ValueError('Epochs parameter is too low. Nothing to do.')
+ return model, compression_scheduler, optimizer, start_epoch, ending_epoch
+
+
+def create_activation_stats_collectors(model, *phases):
+ """Create objects that collect activation statistics.
+
+ This is a utility function that creates two collectors:
+ 1. Fine-grade sparsity levels of the activations
+ 2. L1-magnitude of each of the activation channels
+
+ Args:
+ model - the model on which we want to collect statistics
+ phases - the statistics collection phases: train, valid, and/or test
+
+ WARNING! Enabling activation statsitics collection will significantly slow down training!
+ """
+ class missingdict(dict):
+ """This is a little trick to prevent KeyError"""
+ def __missing__(self, key):
+ return None # note, does *not* set self[key] - we don't want defaultdict's behavior
+
+ genCollectors = lambda: missingdict({
+ "sparsity": SummaryActivationStatsCollector(model, "sparsity",
+ lambda t: 100 * distiller.utils.sparsity(t)),
+ "l1_channels": SummaryActivationStatsCollector(model, "l1_channels",
+ distiller.utils.activation_channels_l1),
+ "apoz_channels": SummaryActivationStatsCollector(model, "apoz_channels",
+ distiller.utils.activation_channels_apoz),
+ "mean_channels": SummaryActivationStatsCollector(model, "mean_channels",
+ distiller.utils.activation_channels_means),
+ "records": RecordsActivationStatsCollector(model, classes=[torch.nn.Conv2d])
+ })
+
+ return {k: (genCollectors() if k in phases else missingdict())
+ for k in ('train', 'valid', 'test')}
+
+
+def save_collectors_data(collectors, directory):
+ """Utility function that saves all activation statistics to disk.
+
+ File type and format of contents are collector-specific.
+ """
+ for name, collector in collectors.items():
+ msglogger.info('Saving data for collector {}...'.format(name))
+ file_path = collector.save(os.path.join(directory, name))
+ msglogger.info("Saved to {}".format(file_path))
+
+
+def load_data(args, fixed_subset=False, sequential=False, load_train=True, load_val=True, load_test=True):
+ train_loader, val_loader, test_loader, _ = apputils.load_data(args.dataset,
+ os.path.expanduser(args.data), args.batch_size,
+ args.workers, args.validation_split, args.deterministic,
+ args.effective_train_size, args.effective_valid_size, args.effective_test_size,
+ fixed_subset, sequential)
+ msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
+ len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler))
+
+ loaders = (train_loader, val_loader, test_loader)
+ flags = (load_train, load_val, load_test)
+ loaders = [loaders[i] for i, flag in enumerate(flags) if flag]
+
+ if len(loaders) == 1:
+ # Unpack the list for convinience
+ loaders = loaders[0]
+ return loaders
+
+
+def early_exit_mode(args):
+ return hasattr(args, 'earlyexit_lossweights') and args.earlyexit_lossweights
+
+
+def train(train_loader, model, criterion, optimizer, epoch,
+ compression_scheduler, loggers, args):
+ """Training-with-compression loop for one epoch.
+
+ For each training step in epoch:
+ compression_scheduler.on_minibatch_begin(epoch)
+ output = model(input)
+ loss = criterion(output, target)
+ compression_scheduler.before_backward_pass(epoch)
+ loss.backward()
+ compression_scheduler.before_parameter_optimization(epoch)
+ optimizer.step()
+ compression_scheduler.on_minibatch_end(epoch)
+ """
+ OVERALL_LOSS_KEY = 'Overall Loss'
+ OBJECTIVE_LOSS_KEY = 'Objective Loss'
+
+ losses = OrderedDict([(OVERALL_LOSS_KEY, tnt.AverageValueMeter()),
+ (OBJECTIVE_LOSS_KEY, tnt.AverageValueMeter())])
+
+ classerr = tnt.ClassErrorMeter(accuracy=True, topk=(1, 5))
+ batch_time = tnt.AverageValueMeter()
+ data_time = tnt.AverageValueMeter()
+
+ # For Early Exit, we define statistics for each exit
+ # So exiterrors is analogous to classerr for the non-Early Exit case
+ if early_exit_mode(args):
+ args.exiterrors = []
+ for exitnum in range(args.num_exits):
+ args.exiterrors.append(tnt.ClassErrorMeter(accuracy=True, topk=(1, 5)))
+
+ total_samples = len(train_loader.sampler)
+ batch_size = train_loader.batch_size
+ steps_per_epoch = math.ceil(total_samples / batch_size)
+ msglogger.info('Training epoch: %d samples (%d per mini-batch)', total_samples, batch_size)
+
+ # Switch to train mode
+ model.train()
+ acc_stats = []
+ end = time.time()
+ for train_step, (inputs, target) in enumerate(train_loader):
+ # Measure data loading time
+ data_time.add(time.time() - end)
+ inputs, target = inputs.to(args.device), target.to(args.device)
+
+ # Execute the forward phase, compute the output and measure loss
+ if compression_scheduler:
+ compression_scheduler.on_minibatch_begin(epoch, train_step, steps_per_epoch, optimizer)
+
+ if not hasattr(args, 'kd_policy') or args.kd_policy is None:
+ output = model(inputs)
+ else:
+ output = args.kd_policy.forward(inputs)
+
+ if not early_exit_mode(args):
+ loss = criterion(output, target)
+ # Measure accuracy
+ classerr.add(output.data, target)
+ acc_stats.append([classerr.value(1), classerr.value(5)])
+ else:
+ # Measure accuracy and record loss
+ loss = earlyexit_loss(output, target, criterion, args)
+ # Record loss
+ losses[OBJECTIVE_LOSS_KEY].add(loss.item())
+
+ if compression_scheduler:
+ # Before running the backward phase, we allow the scheduler to modify the loss
+ # (e.g. add regularization loss)
+ agg_loss = compression_scheduler.before_backward_pass(epoch, train_step, steps_per_epoch, loss,
+ optimizer=optimizer, return_loss_components=True)
+ loss = agg_loss.overall_loss
+ losses[OVERALL_LOSS_KEY].add(loss.item())
+
+ for lc in agg_loss.loss_components:
+ if lc.name not in losses:
+ losses[lc.name] = tnt.AverageValueMeter()
+ losses[lc.name].add(lc.value.item())
+ else:
+ losses[OVERALL_LOSS_KEY].add(loss.item())
+
+ # Compute the gradient and do SGD step
+ optimizer.zero_grad()
+ loss.backward()
+ if compression_scheduler:
+ compression_scheduler.before_parameter_optimization(epoch, train_step, steps_per_epoch, optimizer)
+ optimizer.step()
+ if compression_scheduler:
+ compression_scheduler.on_minibatch_end(epoch, train_step, steps_per_epoch, optimizer)
+
+ # measure elapsed time
+ batch_time.add(time.time() - end)
+ steps_completed = (train_step+1)
+
+ if steps_completed % args.print_freq == 0:
+ # Log some statistics
+ errs = OrderedDict()
+ if not early_exit_mode(args):
+ errs['Top1'] = classerr.value(1)
+ errs['Top5'] = classerr.value(5)
+ else:
+ # for Early Exit case, the Top1 and Top5 stats are computed for each exit.
+ for exitnum in range(args.num_exits):
+ errs['Top1_exit' + str(exitnum)] = args.exiterrors[exitnum].value(1)
+ errs['Top5_exit' + str(exitnum)] = args.exiterrors[exitnum].value(5)
+
+ stats_dict = OrderedDict()
+ for loss_name, meter in losses.items():
+ stats_dict[loss_name] = meter.mean
+ stats_dict.update(errs)
+ stats_dict['LR'] = optimizer.param_groups[0]['lr']
+ stats_dict['Time'] = batch_time.mean
+ stats = ('Performance/Training/', stats_dict)
+
+ params = model.named_parameters() if args.log_params_histograms else None
+ distiller.log_training_progress(stats,
+ params,
+ epoch, steps_completed,
+ steps_per_epoch, args.print_freq,
+ loggers)
+ end = time.time()
+ #return acc_stats
+ # NOTE: this breaks previous behavior, which returned a history of (top1, top5) values
+ return classerr.value(1), classerr.value(5), losses[OVERALL_LOSS_KEY]
+
+def validate(val_loader, model, criterion, loggers, args, epoch=-1):
+ """Model validation"""
+ if epoch > -1:
+ msglogger.info('--- validate (epoch=%d)-----------', epoch)
+ else:
+ msglogger.info('--- validate ---------------------')
+ return _validate(val_loader, model, criterion, loggers, args, epoch)
+
+
+def test(test_loader, model, criterion, loggers, activations_collectors, args):
+ """Model Test"""
+ msglogger.info('--- test ---------------------')
+ if activations_collectors is None:
+ activations_collectors = create_activation_stats_collectors(model, None)
+ with collectors_context(activations_collectors["test"]) as collectors:
+ top1, top5, lossses = _validate(test_loader, model, criterion, loggers, args)
+ distiller.log_activation_statsitics(-1, "test", loggers, collector=collectors['sparsity'])
+ save_collectors_data(collectors, msglogger.logdir)
+ return top1, top5, lossses
+
+
+# Temporary patch until we refactor early-exit handling
+def _is_earlyexit(args):
+ return hasattr(args, 'earlyexit_thresholds') and args.earlyexit_thresholds
+
+def _validate(data_loader, model, criterion, loggers, args, epoch=-1):
+ """Execute the validation/test loop."""
+ losses = {'objective_loss': tnt.AverageValueMeter()}
+ classerr = tnt.ClassErrorMeter(accuracy=True, topk=(1, 5))
+
+ if _is_earlyexit(args):
+ # for Early Exit, we have a list of errors and losses for each of the exits.
+ args.exiterrors = []
+ args.losses_exits = []
+ for exitnum in range(args.num_exits):
+ args.exiterrors.append(tnt.ClassErrorMeter(accuracy=True, topk=(1, 5)))
+ args.losses_exits.append(tnt.AverageValueMeter())
+ args.exit_taken = [0] * args.num_exits
+
+ batch_time = tnt.AverageValueMeter()
+ total_samples = len(data_loader.sampler)
+ batch_size = data_loader.batch_size
+ if args.display_confusion:
+ confusion = tnt.ConfusionMeter(args.num_classes)
+ total_steps = total_samples / batch_size
+ msglogger.info('%d samples (%d per mini-batch)', total_samples, batch_size)
+
+ # Switch to evaluation mode
+ model.eval()
+
+ end = time.time()
+ with torch.no_grad():
+ for validation_step, (inputs, target) in enumerate(data_loader):
+ inputs, target = inputs.to(args.device), target.to(args.device)
+ # compute output from model
+ output = model(inputs)
+
+ if not _is_earlyexit(args):
+ # compute loss
+ loss = criterion(output, target)
+ # measure accuracy and record loss
+ losses['objective_loss'].add(loss.item())
+ classerr.add(output.data, target)
+ if args.display_confusion:
+ confusion.add(output.data, target)
+ else:
+ earlyexit_validate_loss(output, target, criterion, args)
+
+ # measure elapsed time
+ batch_time.add(time.time() - end)
+ end = time.time()
+
+ steps_completed = (validation_step+1)
+ if steps_completed % args.print_freq == 0:
+ if not _is_earlyexit(args):
+ stats = ('',
+ OrderedDict([('Loss', losses['objective_loss'].mean),
+ ('Top1', classerr.value(1)),
+ ('Top5', classerr.value(5))]))
+ else:
+ stats_dict = OrderedDict()
+ stats_dict['Test'] = validation_step
+ for exitnum in range(args.num_exits):
+ la_string = 'LossAvg' + str(exitnum)
+ stats_dict[la_string] = args.losses_exits[exitnum].mean
+ # Because of the nature of ClassErrorMeter, if an exit is never taken during the batch,
+ # then accessing the value(k) will cause a divide by zero. So we'll build the OrderedDict
+ # accordingly and we will not print for an exit error when that exit is never taken.
+ if args.exit_taken[exitnum]:
+ t1 = 'Top1_exit' + str(exitnum)
+ t5 = 'Top5_exit' + str(exitnum)
+ stats_dict[t1] = args.exiterrors[exitnum].value(1)
+ stats_dict[t5] = args.exiterrors[exitnum].value(5)
+ stats = ('Performance/Validation/', stats_dict)
+
+ distiller.log_training_progress(stats, None, epoch, steps_completed,
+ total_steps, args.print_freq, loggers)
+ if not _is_earlyexit(args):
+ msglogger.info('==> Top1: %.3f Top5: %.3f Loss: %.3f\n',
+ classerr.value()[0], classerr.value()[1], losses['objective_loss'].mean)
+
+ if args.display_confusion:
+ msglogger.info('==> Confusion:\n%s\n', str(confusion.value()))
+ return classerr.value(1), classerr.value(5), losses['objective_loss'].mean
+ else:
+ total_top1, total_top5, losses_exits_stats = earlyexit_validate_stats(args)
+ return total_top1, total_top5, losses_exits_stats[args.num_exits-1]
+
+
+def update_training_scores_history(perf_scores_history, model, top1, top5, epoch, num_best_scores):
+ """ Update the list of top training scores achieved so far, and log the best scores so far"""
+
+ model_sparsity, _, params_nnz_cnt = distiller.model_params_stats(model)
+ perf_scores_history.append(distiller.MutableNamedTuple({'params_nnz_cnt': -params_nnz_cnt,
+ 'sparsity': model_sparsity,
+ 'top1': top1, 'top5': top5, 'epoch': epoch}))
+ # Keep perf_scores_history sorted from best to worst
+ # Sort by sparsity as main sort key, then sort by top1, top5 and epoch
+ perf_scores_history.sort(key=operator.attrgetter('params_nnz_cnt', 'top1', 'top5', 'epoch'), reverse=True)
+ for score in perf_scores_history[:num_best_scores]:
+ msglogger.info('==> Best [Top1: %.3f Top5: %.3f Sparsity:%.2f Params: %d on epoch: %d]',
+ score.top1, score.top5, score.sparsity, -score.params_nnz_cnt, score.epoch)
+
+
+def earlyexit_loss(output, target, criterion, args):
+ loss = 0
+ sum_lossweights = 0
+ for exitnum in range(args.num_exits-1):
+ loss += (args.earlyexit_lossweights[exitnum] * criterion(output[exitnum], target))
+ sum_lossweights += args.earlyexit_lossweights[exitnum]
+ args.exiterrors[exitnum].add(output[exitnum].data, target)
+ # handle final exit
+ loss += (1.0 - sum_lossweights) * criterion(output[args.num_exits-1], target)
+ args.exiterrors[args.num_exits-1].add(output[args.num_exits-1].data, target)
+ return loss
+
+
+def earlyexit_validate_loss(output, target, criterion, args):
+ # We need to go through each sample in the batch itself - in other words, we are
+ # not doing batch processing for exit criteria - we do this as though it were batchsize of 1
+ # but with a grouping of samples equal to the batch size.
+ # Note that final group might not be a full batch - so determine actual size.
+ this_batch_size = target.size()[0]
+ earlyexit_validate_criterion = nn.CrossEntropyLoss(reduce=False).to(args.device)
+
+ for exitnum in range(args.num_exits):
+ # calculate losses at each sample separately in the minibatch.
+ args.loss_exits[exitnum] = earlyexit_validate_criterion(output[exitnum], target)
+ # for batch_size > 1, we need to reduce this down to an average over the batch
+ args.losses_exits[exitnum].add(torch.mean(args.loss_exits[exitnum]).cpu())
+
+ for batch_index in range(this_batch_size):
+ earlyexit_taken = False
+ # take the exit using CrossEntropyLoss as confidence measure (lower is more confident)
+ for exitnum in range(args.num_exits - 1):
+ if args.loss_exits[exitnum][batch_index] < args.earlyexit_thresholds[exitnum]:
+ # take the results from early exit since lower than threshold
+ args.exiterrors[exitnum].add(torch.tensor(np.array(output[exitnum].data[batch_index].cpu(), ndmin=2)),
+ torch.full([1], target[batch_index], dtype=torch.long))
+ args.exit_taken[exitnum] += 1
+ earlyexit_taken = True
+ break # since exit was taken, do not affect the stats of subsequent exits
+ # this sample does not exit early and therefore continues until final exit
+ if not earlyexit_taken:
+ exitnum = args.num_exits - 1
+ args.exiterrors[exitnum].add(torch.tensor(np.array(output[exitnum].data[batch_index].cpu(), ndmin=2)),
+ torch.full([1], target[batch_index], dtype=torch.long))
+ args.exit_taken[exitnum] += 1
+
+
+def earlyexit_validate_stats(args):
+ # Print some interesting summary stats for number of data points that could exit early
+ top1k_stats = [0] * args.num_exits
+ top5k_stats = [0] * args.num_exits
+ losses_exits_stats = [0] * args.num_exits
+ sum_exit_stats = 0
+ for exitnum in range(args.num_exits):
+ if args.exit_taken[exitnum]:
+ sum_exit_stats += args.exit_taken[exitnum]
+ msglogger.info("Exit %d: %d", exitnum, args.exit_taken[exitnum])
+ top1k_stats[exitnum] += args.exiterrors[exitnum].value(1)
+ top5k_stats[exitnum] += args.exiterrors[exitnum].value(5)
+ losses_exits_stats[exitnum] += args.losses_exits[exitnum].mean
+ for exitnum in range(args.num_exits):
+ if args.exit_taken[exitnum]:
+ msglogger.info("Percent Early Exit %d: %.3f", exitnum,
+ (args.exit_taken[exitnum]*100.0) / sum_exit_stats)
+ total_top1 = 0
+ total_top5 = 0
+ for exitnum in range(args.num_exits):
+ total_top1 += (top1k_stats[exitnum] * (args.exit_taken[exitnum] / sum_exit_stats))
+ total_top5 += (top5k_stats[exitnum] * (args.exit_taken[exitnum] / sum_exit_stats))
+ msglogger.info("Accuracy Stats for exit %d: top1 = %.3f, top5 = %.3f", exitnum, top1k_stats[exitnum], top5k_stats[exitnum])
+ msglogger.info("Totals for entire network with early exits: top1 = %.3f, top5 = %.3f", total_top1, total_top5)
+ return total_top1, total_top5, losses_exits_stats
+
+
+def evaluate_model(model, criterion, test_loader, loggers, activations_collectors, args, scheduler=None):
+ # This sample application can be invoked to evaluate the accuracy of your model on
+ # the test dataset.
+ # You can optionally quantize the model to 8-bit integer before evaluation.
+ # For example:
+ # python3 compress_classifier.py --arch resnet20_cifar ../data.cifar10 -p=50 --resume-from=checkpoint.pth.tar --evaluate
+
+ if not isinstance(loggers, list):
+ loggers = [loggers]
+
+ if args.quantize_eval:
+ model.cpu()
+ quantizer = quantization.PostTrainLinearQuantizer.from_args(model, args)
+ quantizer.prepare_model(distiller.get_dummy_input(input_shape=model.input_shape))
+ model.to(args.device)
+
+ top1, _, _ = test(test_loader, model, criterion, loggers, activations_collectors, args=args)
+
+ if args.quantize_eval:
+ checkpoint_name = 'quantized'
+ apputils.save_checkpoint(0, args.arch, model, optimizer=None, scheduler=scheduler,
+ name='_'.join([args.name, checkpoint_name]) if args.name else checkpoint_name,
+ dir=msglogger.logdir, extras={'quantized_top1': top1})
+
+def acts_quant_stats_collection(model, criterion, loggers, args):
+ msglogger.info('Collecting quantization calibration stats based on {:.1%} of test dataset'
+ .format(args.qe_calibration))
+ model = distiller.utils.make_non_parallel_copy(model)
+ args.effective_test_size = args.qe_calibration
+ test_loader = load_data(args, load_train=False, load_val=False)
+ test_fn = partial(test, test_loader=test_loader, criterion=criterion,
+ loggers=loggers, args=args, activations_collectors=None)
+ collect_quant_stats(model, test_fn, save_dir=msglogger.logdir,
+ classes=None, inplace_runtime_check=True, disable_inplace_attrs=True)
+
+
+def acts_histogram_collection(model, criterion, loggers, args):
+ msglogger.info('Collecting activation histograms based on {:.1%} of test dataset'
+ .format(args.activation_histograms))
+ model = distiller.utils.make_non_parallel_copy(model)
+ args.effective_test_size = args.activation_histograms
+ test_loader = load_data(args, fixed_subset=True, load_train=False, load_val=False)
+ test_fn = partial(test, test_loader=test_loader, criterion=criterion,
+ loggers=loggers, args=args, activations_collectors=None)
+ collect_histograms(model, test_fn, save_dir=msglogger.logdir,
+ classes=None, nbins=2048, save_hist_imgs=True)
diff --git a/distiller/data_loggers/tbbackend.py b/distiller/data_loggers/tbbackend.py
index ccd077a..95b8834 100755
--- a/distiller/data_loggers/tbbackend.py
+++ b/distiller/data_loggers/tbbackend.py
@@ -19,6 +19,9 @@ Writes logs to a file using a Google's TensorBoard protobuf format.
See: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto
"""
import os
+# Disable FutureWarning from TensorFlow
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
import tensorflow as tf
import numpy as np
diff --git a/distiller/pruning/__init__.py b/distiller/pruning/__init__.py
index abc2525..710c7b5 100755
--- a/distiller/pruning/__init__.py
+++ b/distiller/pruning/__init__.py
@@ -38,7 +38,8 @@ from .ranked_structures_pruner import L1RankedStructureParameterPruner, \
GradientRankedFilterPruner, \
RandomRankedFilterPruner, \
RandomLevelStructureParameterPruner, \
- BernoulliFilterPruner
+ BernoulliFilterPruner, \
+ FMReconstructionChannelPruner
from .baidu_rnn_pruner import BaiduRNNPruner
from .greedy_filter_pruning import greedy_pruner
diff --git a/distiller/pruning/ranked_structures_pruner.py b/distiller/pruning/ranked_structures_pruner.py
index 39e8e99..7b47e8e 100755
--- a/distiller/pruning/ranked_structures_pruner.py
+++ b/distiller/pruning/ranked_structures_pruner.py
@@ -16,18 +16,34 @@
from functools import partial
import numpy as np
+import math
import logging
import torch
+from torch.nn import functional as f
from random import uniform
import distiller
from .pruner import _ParameterPruner
-msglogger = logging.getLogger()
-class RankedStructureParameterPruner(_ParameterPruner):
+__all__ = ["LpRankedStructureParameterPruner",
+ "L1RankedStructureParameterPruner",
+ "L2RankedStructureParameterPruner",
+ "RandomLevelStructureParameterPruner",
+ "ActivationRankedFilterPruner",
+ "ActivationAPoZRankedFilterPruner",
+ "ActivationMeanRankedFilterPruner",
+ "RandomRankedFilterPruner",
+ "BernoulliFilterPruner",
+ "GradientRankedFilterPruner",
+ "FMReconstructionChannelPruner"]
+msglogger = logging.getLogger(__name__)
+
+
+class _RankedStructureParameterPruner(_ParameterPruner):
"""Base class for pruning structures by ranking them.
"""
- def __init__(self, name, group_type, desired_sparsity, weights, group_dependency=None):
+ def __init__(self, name, group_type, desired_sparsity, weights,
+ group_dependency=None, group_size=1, rounding_fn=math.floor, noise=0.):
super().__init__(name)
self.group_type = group_type
self.group_dependency = group_dependency
@@ -36,6 +52,9 @@ class RankedStructureParameterPruner(_ParameterPruner):
self.leader_binary_map = None
self.last_target_sparsity = None
self.desired_sparsity = desired_sparsity
+ self.group_size = group_size
+ self.rounding_fn = rounding_fn
+ self.noise = noise
def leader(self):
# The "leader" is the first weights-tensor in the list
@@ -83,7 +102,7 @@ l1_magnitude = partial(torch.norm, p=1)
l2_magnitude = partial(torch.norm, p=2)
-class LpRankedStructureParameterPruner(RankedStructureParameterPruner):
+class LpRankedStructureParameterPruner(_RankedStructureParameterPruner):
"""Uses Lp-norm to rank and prune structures.
This class prunes to a prescribed percentage of structured-sparsity (level pruning), by
@@ -92,15 +111,17 @@ class LpRankedStructureParameterPruner(RankedStructureParameterPruner):
See also: https://en.wikipedia.org/wiki/Lp_space#The_p-norm_in_finite_dimensions
"""
def __init__(self, name, group_type, desired_sparsity, weights,
- group_dependency=None, kwargs=None, magnitude_fn=None):
- super().__init__(name, group_type, desired_sparsity, weights, group_dependency)
+ group_dependency=None, kwargs=None, magnitude_fn=None,
+ noise=0.0, group_size=1, rounding_fn=math.floor):
+ super().__init__(name, group_type, desired_sparsity, weights,
+ group_dependency, group_size, rounding_fn, noise)
if group_type not in ['3D', 'Filters', 'Channels', 'Rows', 'Blocks']:
raise ValueError("Structure {} was requested but "
"currently ranking of this shape is not supported".
format(group_type))
assert magnitude_fn is not None
self.magnitude_fn = magnitude_fn
-
+
if group_type == 'Blocks':
try:
self.block_shape = kwargs['block_shape']
@@ -111,9 +132,9 @@ class LpRankedStructureParameterPruner(RankedStructureParameterPruner):
if fraction_to_prune == 0:
return
if self.group_type in ['3D', 'Filters']:
- group_pruning_fn = self.rank_and_prune_filters
+ group_pruning_fn = partial(self.rank_and_prune_filters, noise=self.noise)
elif self.group_type == 'Channels':
- group_pruning_fn = partial(self.rank_and_prune_channels)
+ group_pruning_fn = partial(self.rank_and_prune_channels, noise=self.noise)
elif self.group_type == 'Rows':
group_pruning_fn = self.rank_and_prune_rows
elif self.group_type == 'Blocks':
@@ -121,44 +142,65 @@ class LpRankedStructureParameterPruner(RankedStructureParameterPruner):
binary_map = group_pruning_fn(fraction_to_prune, param, param_name,
zeros_mask_dict, model, binary_map,
- magnitude_fn=self.magnitude_fn)
+ magnitude_fn=self.magnitude_fn,
+ group_size=self.group_size)
return binary_map
@staticmethod
- def rank_and_prune_channels(fraction_to_prune, param, param_name=None,
- zeros_mask_dict=None, model=None, binary_map=None, magnitude_fn=l1_magnitude):
- def rank_channels(fraction_to_prune, param):
- num_filters = param.size(0)
- num_channels = param.size(1)
- kernel_size = param.size(2) * param.size(3)
-
- # First, reshape the weights tensor such that each channel (kernel) in the original
- # tensor, is now a row in the 2D tensor.
- view_2d = param.view(-1, kernel_size)
- # Next, compute the sums of each kernel
- kernel_mags = magnitude_fn(view_2d, dim=1)
- # Now group by channels
- k_sums_mat = kernel_mags.view(num_filters, num_channels).t()
- channel_mags = k_sums_mat.mean(dim=1)
- k = int(fraction_to_prune * channel_mags.size(0))
- if k == 0:
- msglogger.info("Too few channels (%d)- can't prune %.1f%% channels",
- num_channels, 100*fraction_to_prune)
- return None, None
+ def rank_channels(magnitude_fn, fraction_to_prune, param, group_size, rounding_fn, noise):
+ assert len(param.shape) == 4
+ num_filters, num_channels = param.size(0), param.size(1)
+ kernel_size = param.size(2) * param.size(3)
- bottomk, _ = torch.topk(channel_mags, k, largest=False, sorted=True)
- return bottomk, channel_mags
+ # First, reshape the weights tensor such that each channel (kernel) in the original
+ # tensor, is now a row in the 2D tensor.
+ view_2d = param.view(-1, kernel_size)
+ # Next, compute the sums of each kernel
+ kernel_mags = magnitude_fn(view_2d, dim=1)
+ # Now group by channels
+ k_sums_mat = kernel_mags.view(num_filters, num_channels).t()
+ channel_mags = k_sums_mat.mean(dim=1)
+
+ # TODO: the code below computes L1 in a simple manner - extend this for other norms
+ #channel_mags = torch.abs(param).sum((0, 2, 3))
+
+ # Round the number of channels to prune, (floor/ceil) to the nearest integer.
+ k = rounding_fn(fraction_to_prune * num_channels)
+ k = int(rounding_fn(k * 1. / group_size) * group_size)
+
+ # We can't allow removing all of the channels! --
+ # Except when the fraction_to_prune is explicitly instructing us to do so.
+ if k == num_channels and fraction_to_prune != 1.0:
+ k = num_channels - group_size
+ if k == 0:
+ msglogger.info("Too few channels (%d)- can't prune %.1f%% channels",
+ num_channels, 100*fraction_to_prune)
+ return None, None
+
+ if noise and uniform(0, 1) <= noise:
+ #msglogger.info("%sRankedStructureParameterPruner - param: %s - randomly choosing channels",
+ # threshold_type, param_name)
+ channel_mags *= torch.randn_like(channel_mags)
+
+ bottomk, _ = torch.topk(channel_mags, k, largest=False, sorted=True)
+ return bottomk, channel_mags
- def binary_map_to_mask(binary_map, param):
- num_filters = param.size(0)
- num_channels = param.size(1)
- a = binary_map.expand(num_filters, num_channels)
- c = a.unsqueeze(-1)
- d = c.expand(num_filters, num_channels, param.size(2) * param.size(3)).contiguous()
- return d.view(num_filters, num_channels, param.size(2), param.size(3))
+ @staticmethod
+ def ch_binary_map_to_mask(binary_map, param):
+ num_filters, num_channels = param.size(0), param.size(1)
+ a = binary_map.expand(num_filters, num_channels)
+ c = a.unsqueeze(-1)
+ d = c.expand(num_filters, num_channels, param.size(2) * param.size(3)).contiguous()
+ return d.view(num_filters, num_channels, param.size(2), param.size(3))
+
+ @staticmethod
+ def rank_and_prune_channels(fraction_to_prune, param, param_name=None, zeros_mask_dict=None,
+ model=None, binary_map=None, magnitude_fn=l1_magnitude,
+ noise=0.0, group_size=1, rounding_fn=math.floor):
if binary_map is None:
- bottomk_channels, channel_mags = rank_channels(fraction_to_prune, param)
+ bottomk_channels, channel_mags = LpRankedStructureParameterPruner.rank_channels(
+ magnitude_fn, fraction_to_prune, param, group_size, rounding_fn, noise)
if bottomk_channels is None:
# Empty list means that fraction_to_prune is too low to prune anything
return
@@ -167,7 +209,7 @@ class LpRankedStructureParameterPruner(RankedStructureParameterPruner):
threshold_type = 'L1' if magnitude_fn == l1_magnitude else 'L2'
if zeros_mask_dict is not None:
- zeros_mask_dict[param_name].mask = binary_map_to_mask(binary_map, param)
+ zeros_mask_dict[param_name].mask = LpRankedStructureParameterPruner.ch_binary_map_to_mask(binary_map, param)
msglogger.info("%sRankedStructureParameterPruner - param: %s pruned=%.3f goal=%.3f (%d/%d)",
threshold_type, param_name,
distiller.sparsity_ch(zeros_mask_dict[param_name].mask),
@@ -175,41 +217,56 @@ class LpRankedStructureParameterPruner(RankedStructureParameterPruner):
return binary_map
@staticmethod
- def rank_and_prune_filters(fraction_to_prune, param, param_name,
- zeros_mask_dict, model=None, binary_map=None, magnitude_fn=l1_magnitude):
+ def rank_and_prune_filters(fraction_to_prune, param, param_name, zeros_mask_dict,
+ model=None, binary_map=None, magnitude_fn=l1_magnitude,
+ noise=0.0, group_size=1, rounding_fn=math.floor):
assert param.dim() == 4, "This pruning is only supported for 4D weights"
threshold = None
threshold_type = 'L1' if magnitude_fn == l1_magnitude else 'L2'
+ num_filters = param.size(0)
+ num_filters_to_prune = rounding_fn(fraction_to_prune * num_filters)
+ num_filters_to_prune = int(rounding_fn(num_filters_to_prune * 1. / group_size) * group_size)
+ # We can't allow removing all of the filters! --
+ # Except when the fraction_to_prune is explicitly instructing us to do so.
+ if num_filters_to_prune == num_filters and fraction_to_prune != 1.0:
+ num_filters_to_prune = num_filters - group_size # We can't allow removing all of the filters!
+
if binary_map is None:
# First we rank the filters
- view_filters = param.view(param.size(0), -1)
+ view_filters = param.view(num_filters, -1)
filter_mags = magnitude_fn(view_filters, dim=1)
- topk_filters = int(fraction_to_prune * filter_mags.size(0))
- if topk_filters == 0:
+
+ if noise and uniform(0, 1) <= noise:
+ msglogger.info("%sRankedStructureParameterPruner - param: %s - randomly choosing filters",
+ threshold_type, param_name)
+ filter_mags *= torch.randn_like(filter_mags)
+
+ if num_filters_to_prune == 0:
msglogger.info("Too few filters - can't prune %.1f%% filters", 100*fraction_to_prune)
return
- bottomk, _ = torch.topk(filter_mags, topk_filters, largest=False, sorted=True)
+ bottomk, _ = torch.topk(filter_mags, num_filters_to_prune, largest=False, sorted=True)
threshold = bottomk[-1]
msglogger.info("%sRankedStructureParameterPruner - param: %s pruned=(%d/%d)",
threshold_type, param_name,
- topk_filters, filter_mags.size(0))
- # Then we threshold
+ num_filters_to_prune, filter_mags.size(0))
+
+ # Now apply a threshold
mask, binary_map = distiller.group_threshold_mask(param, 'Filters', threshold, threshold_type, binary_map)
+
if zeros_mask_dict is not None:
zeros_mask_dict[param_name].mask = mask
msglogger.info("%sRankedStructureParameterPruner - param: %s pruned=%.3f goal=%.3f",
threshold_type, param_name,
distiller.sparsity(mask),
fraction_to_prune)
-
- # Compensate for dropping filters
- #param.data /= float(distiller.sparsity(mask))
+ # param.data = torch.randn_like(param)
return binary_map
@staticmethod
def rank_and_prune_rows(fraction_to_prune, param, param_name,
- zeros_mask_dict, model=None, binary_map=None, magnitude_fn=l1_magnitude):
+ zeros_mask_dict, model=None, binary_map=None,
+ magnitude_fn=l1_magnitude, group_size=1):
"""Prune the rows of a matrix, based on ranked L1-norms of the matrix rows.
PyTorch stores the weights matrices in a transposed format. I.e. before performing GEMM, a matrix is
@@ -241,7 +298,8 @@ class LpRankedStructureParameterPruner(RankedStructureParameterPruner):
@staticmethod
def rank_and_prune_blocks(fraction_to_prune, param, param_name=None, zeros_mask_dict=None,
- model=None, binary_map=None, block_shape=None, magnitude_fn=l1_magnitude):
+ model=None, binary_map=None, block_shape=None,
+ magnitude_fn=l1_magnitude, group_size=1):
"""Block-wise pruning for 4D tensors.
The block shape is specified using a tuple: [block_repetitions, block_depth, block_height, block_width].
@@ -270,8 +328,7 @@ class LpRankedStructureParameterPruner(RankedStructureParameterPruner):
if distiller.volume(param) % super_block_volume != 0:
raise ValueError("The super-block size must divide the weight tensor exactly.")
- num_filters = param.size(0)
- num_channels = param.size(1)
+ num_filters, num_channels = param.size(0), param.size(1)
kernel_size = param.size(2) * param.size(3)
if block_depth > 1:
@@ -328,9 +385,11 @@ class L1RankedStructureParameterPruner(LpRankedStructureParameterPruner):
This class prunes to a prescribed percentage of structured-sparsity (level pruning).
"""
def __init__(self, name, group_type, desired_sparsity, weights,
- group_dependency=None, kwargs=None):
- super().__init__(name, group_type, desired_sparsity, weights,
- group_dependency, kwargs, magnitude_fn=l1_magnitude)
+ group_dependency=None, kwargs=None, noise=0.0,
+ group_size=1, rounding_fn=math.floor):
+ super().__init__(name, group_type, desired_sparsity, weights, group_dependency,
+ kwargs, magnitude_fn=l1_magnitude, noise=noise,
+ group_size=group_size, rounding_fn=rounding_fn)
class L2RankedStructureParameterPruner(LpRankedStructureParameterPruner):
@@ -339,9 +398,11 @@ class L2RankedStructureParameterPruner(LpRankedStructureParameterPruner):
This class prunes to a prescribed percentage of structured-sparsity (level pruning).
"""
def __init__(self, name, group_type, desired_sparsity, weights,
- group_dependency=None, kwargs=None):
- super().__init__(name, group_type, desired_sparsity, weights,
- group_dependency, kwargs, magnitude_fn=l2_magnitude)
+ group_dependency=None, kwargs=None, noise=0.0,
+ group_size=1, rounding_fn=math.floor):
+ super().__init__(name, group_type, desired_sparsity, weights, group_dependency,
+ kwargs, magnitude_fn=l2_magnitude, noise=noise,
+ group_size=group_size, rounding_fn=rounding_fn)
class RandomLevelStructureParameterPruner(L1RankedStructureParameterPruner):
@@ -361,7 +422,7 @@ class RandomLevelStructureParameterPruner(L1RankedStructureParameterPruner):
return uniform(self.sparsity_range[0], self.sparsity_range[1])
-def mask_from_filter_order(filters_ordered_by_criterion, param, num_filters, binary_map):
+def _mask_from_filter_order(filters_ordered_by_criterion, param, num_filters, binary_map):
if binary_map is None:
binary_map = torch.zeros(num_filters).cuda()
binary_map[filters_ordered_by_criterion] = 1
@@ -370,7 +431,7 @@ def mask_from_filter_order(filters_ordered_by_criterion, param, num_filters, bin
return expanded.view(param.shape), binary_map
-class ActivationRankedFilterPruner(RankedStructureParameterPruner):
+class ActivationRankedFilterPruner(_RankedStructureParameterPruner):
"""Base class for pruners ranking convolution filters by some quality criterion of the
corresponding feature-map channels (e.g. mean channel activation L1 value).
"""
@@ -385,7 +446,8 @@ class ActivationRankedFilterPruner(RankedStructureParameterPruner):
if fraction_to_prune == 0:
return
binary_map = self.rank_and_prune_filters(fraction_to_prune, param, param_name,
- zeros_mask_dict, model, binary_map)
+ zeros_mask_dict, model, binary_map,
+ self.rounding_fn)
return binary_map
def rank_and_prune_filters(self, fraction_to_prune, param, param_name, zeros_mask_dict, model, binary_map=None):
@@ -411,7 +473,7 @@ class ActivationRankedFilterPruner(RankedStructureParameterPruner):
# Sort from low to high, and remove the bottom 'num_filters_to_prune' filters
filters_ordered_by_criterion = np.argsort(quality_criterion)[:-num_filters_to_prune]
- mask, binary_map = mask_from_filter_order(filters_ordered_by_criterion, param, num_filters, binary_map)
+ mask, binary_map = _mask_from_filter_order(filters_ordered_by_criterion, param, num_filters, binary_map)
zeros_mask_dict[param_name].mask = mask
msglogger.info("ActivationL1RankedStructureParameterPruner - param: %s pruned=%.3f goal=%.3f (%d/%d)",
@@ -446,7 +508,7 @@ class ActivationMeanRankedFilterPruner(ActivationRankedFilterPruner):
return 'mean_channels'
-class RandomRankedFilterPruner(RankedStructureParameterPruner):
+class RandomRankedFilterPruner(_RankedStructureParameterPruner):
"""A Random ranking of filters.
This is used for sanity testing of other algorithms.
@@ -471,7 +533,7 @@ class RandomRankedFilterPruner(RankedStructureParameterPruner):
return
filters_ordered_randomly = np.random.permutation(num_filters)[:-num_filters_to_prune]
- mask, binary_map = mask_from_filter_order(filters_ordered_randomly, param, num_filters, binary_map)
+ mask, binary_map = _mask_from_filter_order(filters_ordered_randomly, param, num_filters, binary_map)
zeros_mask_dict[param_name].mask = mask
msglogger.info("RandomRankedFilterPruner - param: %s pruned=%.3f goal=%.3f (%d/%d)",
@@ -481,7 +543,7 @@ class RandomRankedFilterPruner(RankedStructureParameterPruner):
return binary_map
-class BernoulliFilterPruner(RankedStructureParameterPruner):
+class BernoulliFilterPruner(_RankedStructureParameterPruner):
"""A Bernoulli probability for dropping each filter.
This is can be used for random filter-dropping algorithms (e.g. DropFilter)
@@ -504,7 +566,7 @@ class BernoulliFilterPruner(RankedStructureParameterPruner):
keep_prob = 1 - fraction_to_prune
if binary_map is None:
binary_map = torch.bernoulli(torch.as_tensor([keep_prob] * num_filters))
- mask, _ = mask_from_filter_order(None, param, num_filters, binary_map)
+ mask, _ = _mask_from_filter_order(None, param, num_filters, binary_map)
mask = mask.to(param.device)
# Compensate for dropping filters
pruning_factor = binary_map.sum() / num_filters
@@ -518,7 +580,7 @@ class BernoulliFilterPruner(RankedStructureParameterPruner):
return binary_map
-class GradientRankedFilterPruner(RankedStructureParameterPruner):
+class GradientRankedFilterPruner(_RankedStructureParameterPruner):
"""
"""
def __init__(self, name, group_type, desired_sparsity, weights, group_dependency=None):
@@ -550,7 +612,7 @@ class GradientRankedFilterPruner(RankedStructureParameterPruner):
# Sort from high to low, and remove the bottom 'num_filters_to_prune' filters
filters_ordered_by_gradient = np.argsort(-weighted_gradients.detach().cpu().numpy())[:-num_filters_to_prune]
- mask, binary_map = mask_from_filter_order(filters_ordered_by_gradient, param, num_filters, binary_map)
+ mask, binary_map = _mask_from_filter_order(filters_ordered_by_gradient, param, num_filters, binary_map)
zeros_mask_dict[param_name].mask = mask
msglogger.info("GradientRankedFilterPruner - param: %s pruned=%.3f goal=%.3f (%d/%d)",
@@ -558,3 +620,196 @@ class GradientRankedFilterPruner(RankedStructureParameterPruner):
distiller.sparsity_3D(zeros_mask_dict[param_name].mask),
fraction_to_prune, num_filters_to_prune, num_filters)
return binary_map
+
+
+from sklearn.linear_model import LinearRegression
+
+def _least_square_sklearn(X, Y):
+ model = LinearRegression(fit_intercept=False)
+ model.fit(X, Y)
+ return model.coef_
+
+
+def _param_name_2_layer_name(param_name):
+ """Convert a weights tensor's name to the name of the layer using the tensor.
+
+ By convention, PyTorch modules name their weights parameters as self.weight
+ (see for example: torch.nn.modules.conv) which means that their fully-qualified
+ name when enumerating a model's parameters is the modules name followed by '.weight'.
+ We exploit this convention to convert a weights tensor name to the fully-qualified
+ module name."""
+ return param_name[:-len('.weight')]
+
+
+class FMReconstructionChannelPruner(_RankedStructureParameterPruner):
+ """Uses feature-map (channel) reconstruction to prune weights tensors.
+
+ The idea behind this pruner is to find a reduced subset of the weights, which best
+ reconstructs the output of a given layer. To choose the subset of the weights,
+ we use a provided magnitude function to rank the channels of a weights tensor.
+ Removing channels from a Convolution layer's weights, means that the layer's input
+ is also reduced.
+ We aim to estimate the minimum mean squared error (MMSE) of the reconstructed outputs,
+ given a size-reduced input. The coefficients of the solution to MMSE are then used as
+ the new weights of the Convolution layer.
+
+ You must invoke function collect_intermediate_featuremap_samples() prior to using a
+ FMReconstructionChannelPruner. Pass in your model, forward_fn, and module_filter_fn.
+ For the fm_caching_fwd_hook argument of collect_intermediate_featuremap_samples, pass
+ FMReconstructionChannelPruner.cache_featuremaps_fwd_hook.
+
+ We thank Prof. Han (https://songhan.mit.edu) and his team for their help with
+ this implementation.
+
+ A variant of this technique was used in [1] and [2].
+
+ [1] Channel Pruning for Accelerating Very Deep Neural Networks.
+ Yihui He, Xiangyu Zhang, Jian Sun.
+ arXiv:1707.06168
+ [2] AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
+ Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han
+ arXiv:1802.03494
+ """
+
+ @staticmethod
+ def cache_featuremaps_fwd_hook(module, input, output, intermediate_fms, n_points_per_fm):
+ """Create a cached dictionary of each layer's input and output feature-maps.
+
+ For reconstruction of weights, we need to collect pairs of (layer_input, layer_output)
+ using a sample subset of the input dataset.
+ This is a forward-hook function, invoked from forward_hooks of Convolution layers.
+ Use this in conjunction with distiller.features_collector.collect_intermediate_featuremap_samples,
+ which orchestrates the process of feature-map collection.
+
+ This foward-hook samples random points.
+
+ After collecting the feature-map samples, distiller.FMReconstructionChannelPruner can be used.
+
+ Arguments:
+ module - the module who's forward_hook is invoked
+ input, output - the input and output arguments to the forward_hook
+ intermediate_fms - a dictionary of lists of feature-map samples, per layer
+ (use module.distiller_name as key)
+ n_points_per_fm - number of points to sample, per feature-map.
+ """
+ def im2col(x, conv):
+ x_unfold = f.unfold(x, kernel_size=conv.kernel_size, stride=conv.stride, padding=conv.padding)
+ return x_unfold
+
+ # Sample random (uniform) points in each feature-map.
+ # This method is biased toward small feature-maps.
+ randx = np.random.randint(0, output.size(2), n_points_per_fm)
+ randy = np.random.randint(0, output.size(3), n_points_per_fm)
+
+ X = input[0]
+ if module.kernel_size == (1,1):
+ X = X[:, :, randx, randy].detach().cpu().clone()
+ Y = output[:, :, randx, randy].detach().cpu().clone()
+ else:
+ w, h = X.size(2), X.size(3)
+ X = im2col(X.detach().cpu().clone(), module).squeeze()
+ w_out = output.size(2)
+ pts = randx * w_out + randy
+ X = X[:, :, pts].detach().cpu().clone()
+ Y = output[:, :, randx, randy].detach().cpu().clone()
+
+ # Preprocess the outputs: transpose the batch and channel dimensions, create a flattened view, and transpose.
+ # The outputs originally have shape: (batch size, num channels, feature-map width, feature-map height).
+ Y = Y.view(Y.size(0), Y.size(1), -1)
+ Y = Y.transpose(2, 1)
+ Y = Y.contiguous().view(-1, Y.size(2))
+
+ intermediate_fms['output_fms'][module.distiller_name].append(Y)
+ intermediate_fms['input_fms'][module.distiller_name].append(X)
+
+ def __init__(self, name, group_type, desired_sparsity, weights,
+ group_dependency=None, kwargs=None, magnitude_fn=l1_magnitude,
+ group_size=1, rounding_fn=math.floor, ranking_noise=0.):
+ super().__init__(name, group_type, desired_sparsity, weights, group_dependency,
+ group_size=group_size, rounding_fn=rounding_fn, noise=ranking_noise)
+ if group_type != "Channels":
+ raise ValueError("Structure {} was requested but "
+ "currently ranking of this shape is not supported".
+ format(group_type))
+ assert magnitude_fn is not None
+ self.magnitude_fn = magnitude_fn
+
+ def prune_group(self, fraction_to_prune, param, param_name, zeros_mask_dict, model=None, binary_map=None):
+ if fraction_to_prune == 0:
+ return
+ binary_map = self.rank_and_prune_channels(fraction_to_prune, param, param_name,
+ zeros_mask_dict, model, binary_map,
+ group_size=self.group_size,
+ rounding_fn=self.rounding_fn,
+ noise=self.noise)
+ return binary_map
+
+ @staticmethod
+ def rank_and_prune_channels(fraction_to_prune, param, param_name=None,
+ zeros_mask_dict=None, model=None, binary_map=None,
+ magnitude_fn=l2_magnitude, group_size=1, rounding_fn=math.floor,
+ noise=0):
+ assert binary_map is None
+ if binary_map is None:
+ bottomk_channels, channel_mags = LpRankedStructureParameterPruner.rank_channels(
+ magnitude_fn, fraction_to_prune, param, group_size, rounding_fn, noise)
+ # Todo: this little piece of code can be refactored
+ if bottomk_channels is None:
+ # Empty list means that fraction_to_prune is too low to prune anything
+ return
+
+ threshold = bottomk_channels[-1]
+ binary_map = channel_mags.gt(threshold)
+
+ # These are the indices of channels we want to keep
+ indices = binary_map.nonzero().squeeze()
+ if len(indices.shape) == 0:
+ indices = indices.expand(1)
+
+ # Find the module representing this layer
+ distiller.assign_layer_fq_names(model)
+ layer_name = _param_name_2_layer_name(param_name)
+ conv = distiller.find_module_by_fq_name(model, layer_name)
+ try:
+ Y = model.intermediate_fms['output_fms'][layer_name]
+ X = model.intermediate_fms['input_fms'][layer_name]
+ except AttributeError:
+ raise ValueError("To use FMReconstructionChannelPruner you must first collect input statistics")
+
+ # We need to remove the chosen weights channels. Because we are using
+ # min(MSE) to compute the weights, we need to start by removing feature-map
+ # channels from the input. Then we perform the MSE regression to generate
+ # a smaller weights tensor.
+ if conv.kernel_size == (1,1):
+ X = X[:, binary_map, :]
+ X = X.transpose(1, 2)
+ X = X.contiguous().view(-1, X.size(2))
+ else:
+ # X is (batch, ck^2, num_pts)
+ # we want: (batch, c, k^2, num_pts)
+ X = X.view(X.size(0), -1, np.prod(conv.kernel_size), X.size(2))
+ X = X[:,binary_map,:,:]
+ X = X.view(X.size(0), -1, X.size(3))
+ X = X.transpose(1, 2)
+ X = X.contiguous().view(-1, X.size(2))
+
+ # Approximate the weights given input-FMs and output-FMs
+ new_w = _least_square_sklearn(X, Y)
+ new_w = torch.from_numpy(new_w) # shape: (num_filters, num_non_masked_channels * k^2)
+ cnt_retained_channels = binary_map.sum()
+
+ # Expand the weights back to their original size,
+ new_w = new_w.contiguous().view(param.size(0), cnt_retained_channels, param.size(2), param.size(3))
+
+ # Copy the weights that we learned from minimizing the feature-maps least squares error,
+ # to our actual weights tensor.
+ param.detach()[:,indices,:,:] = new_w.type(param.type())
+
+ if zeros_mask_dict is not None:
+ binary_map = binary_map.type(param.type())
+ zeros_mask_dict[param_name].mask = LpRankedStructureParameterPruner.ch_binary_map_to_mask(binary_map, param)
+ msglogger.info("FMReconstructionChannelPruner - param: %s pruned=%.3f goal=%.3f (%d/%d)",
+ param_name,
+ distiller.sparsity_ch(zeros_mask_dict[param_name].mask),
+ fraction_to_prune, binary_map.sum().item(), param.size(1))
+ return binary_map
diff --git a/distiller/summary_graph.py b/distiller/summary_graph.py
index 0c43a3a..5df0c6b 100755
--- a/distiller/summary_graph.py
+++ b/distiller/summary_graph.py
@@ -337,9 +337,11 @@ class SummaryGraph(object):
group = op['attrs']['group']
else:
kernel_size, group = 1, 1
- n_ifm = self.param_shape(conv_in)[1] / group
+ n_ifm = self.param_shape(conv_in)[1]
n_ofm = self.param_shape(conv_out)[1]
- weights_vol = kernel_size * n_ifm * n_ofm
+ weights_vol = kernel_size * n_ifm * n_ofm / group
+ op['attrs']['n_ifm'] = n_ifm
+ op['attrs']['n_ofm'] = n_ofm
op['attrs']['footprint'] = ofm_vol + ifm_vol + weights_vol
op['attrs']['fm_vol'] = ofm_vol + ifm_vol
op['attrs']['weights_vol'] = weights_vol
diff --git a/distiller/thinning.py b/distiller/thinning.py
index 239bdbc..d06fe3a 100755
--- a/distiller/thinning.py
+++ b/distiller/thinning.py
@@ -84,7 +84,7 @@ def directives_equal(d1, d2):
return d1[0] == d2[0] and torch.equal(d1[1], d2[1])
if len(d1) == 4:
e = all(d1[i] == d2[i] for i in (0, 2, 3)) and torch.equal(d1[1], d2[1])
- msglogger.info("{}: \n{}\n{}".format(e, d1, d2))
+ msglogger.debug("{}: \n{}\n{}".format(e, d1, d2))
return e
assert ValueError("Unsupported directive length")
@@ -168,8 +168,8 @@ def find_nonzero_channels(param, param_name):
nonzero_channels = torch.nonzero(k_sums_mat.abs().sum(dim=1))
if num_channels > nonzero_channels.nelement():
- msglogger.info("In tensor %s found %d/%d zero channels", param_name,
- num_channels - nonzero_channels.nelement(), num_channels)
+ msglogger.debug("In tensor %s found %d/%d zero channels", param_name,
+ num_channels - nonzero_channels.nelement(), num_channels)
return nonzero_channels
# Todo: consider removing this function
@@ -264,7 +264,7 @@ def create_thinning_recipe_channels(sgraph, model, zeros_mask_dict):
append_bn_thinning_directive(thinning_recipe, layers, bn_layer,
len_thin_features=num_nnz_channels, thin_features=indices)
- msglogger.info("Invoking create_thinning_recipe_channels")
+ msglogger.debug("Invoking create_thinning_recipe_channels")
thinning_recipe = ThinningRecipe(modules={}, parameters={})
layers = {mod_name: m for mod_name, m in model.named_modules()}
@@ -363,7 +363,7 @@ def create_thinning_recipe_filters(sgraph, model, zeros_mask_dict):
append_bn_thinning_directive(thinning_recipe, layers, bn_layers[0],
len_thin_features=num_nnz_filters, thin_features=indices)
- msglogger.info("Invoking create_thinning_recipe_filters")
+ msglogger.debug("Invoking create_thinning_recipe_filters")
thinning_recipe = ThinningRecipe(modules={}, parameters={})
layers = {mod_name: m for mod_name, m in model.named_modules()}
@@ -384,8 +384,8 @@ def create_thinning_recipe_filters(sgraph, model, zeros_mask_dict):
msglogger.debug("Skipping {} shape={}".format(param_name, param.shape))
continue
- msglogger.info("In tensor %s found %d/%d zero filters", param_name,
- num_filters - num_nnz_filters, num_filters)
+ msglogger.debug("In tensor %s found %d/%d zero filters", param_name,
+ num_filters - num_nnz_filters, num_filters)
handle_layer(layer_name, param_name, num_nnz_filters)
return thinning_recipe
@@ -441,7 +441,7 @@ def execute_thinning_recipes_list(model, zeros_mask_dict, recipe_list):
for i, recipe in enumerate(recipe_list):
msglogger.debug("Executing recipe %d:" % i)
execute_thinning_recipe(model, zeros_mask_dict, recipe, optimizer=None, loaded_from_file=True)
- msglogger.info("Executed %d recipes" % len(recipe_list))
+ msglogger.debug("Executed %d recipes" % len(recipe_list))
def optimizer_thinning(optimizer, param, dim, indices, new_shape=None):
diff --git a/examples/auto_compression/README.md b/examples/auto_compression/README.md
new file mode 100755
index 0000000..ddd513c
--- /dev/null
+++ b/examples/auto_compression/README.md
@@ -0,0 +1,6 @@
+# Automated Compression
+
+This directory contains example applications of various methods to automate the DNN compression process.<br>
+We will slowly release implementations of these methods.
+
+- ### [AMC - AutoML for Model Compression](./amc/README.md)
\ No newline at end of file
diff --git a/examples/automated_deep_compression/ddpg/__init__.py b/examples/auto_compression/__init__.py
old mode 100644
new mode 100755
similarity index 100%
rename from examples/automated_deep_compression/ddpg/__init__.py
rename to examples/auto_compression/__init__.py
diff --git a/examples/auto_compression/amc/README.md b/examples/auto_compression/amc/README.md
new file mode 100755
index 0000000..acf8ed2
--- /dev/null
+++ b/examples/auto_compression/amc/README.md
@@ -0,0 +1,38 @@
+# AMC: AutoML for Model Compression and Acceleration on Mobile Devices
+
+This is a loose implementation of AMC, that allows you to change models and agents and use Jupyter notebooks to examine the learning process in detail. However details may note exactly match the [implementation](https://github.com/mit-han-lab/amc-compressed-models) from the original paper.
+
+Caveats:
+- This is still a WiP and some information is still missing, such as setup instruction and further documentation. These will be added with time.
+- Some Jupyter examples may contain mistakes in the example command-lines. Please let us know of any mistakes and bugs you find.
+## Notebook examples of AMC applied to compress CNNs:
+These notebooks will help visualize and review the results of recreating the results of AMC (resource-constrained compression.).
+
+<div style="text-align: center;"> <img src="imgs/amc_worksheet.png"></div>
+
+- [AMC worksheet](./jupyter/amc_worksheet.ipynb): work through visualizations of different aspects of AMC learning
+- [Random](./jupyter/amc_random.ipynb)
+- [Plain20](./jupyter/amc_plain20.ipynb)
+- [Resnet20](./jupyter/amc_resnet20.ipynb)
+- [Resnet 56]()
+- [Mobilenet v1]()
+- [Mobilenet v2]()
+
+## At a glance
+
+| Model | FLOPs | Baseline Top1 | AMC paper<br>(Top1 after FT) | Baseline Top1 | AMC Distiller
+| --- | ---: | ---: | ---: | ---: | ---: |
+| Plain20 | 50% | 90.5 | 90.2 (-0.3) | 90.55 | 90.04 (-0.4)
+| Resnet20 | 50%| 91.78 | - |
+| Resnet56 | 50% |92.8 | 91.9 (-0.9)
+| Mobilenet v1 | 50% | 70.6 | 70.2 (-0.4)
+| Mobilenet v2 | 50% | 71.8 | 70.8 (-1.0)
+
+AMC [1] trains a Deep Deterministic Policy Gradient (DDPG) RL agent to compress DNNs. For the implementation of DDPG we're using [Coach](https://github.com/NervanaSystems/coach) or Prof. Han's team's [implementation](https://github.com/mit-han-lab/amc-compressed-models) . We wrap Distiller in an OpenAI [Gym environment API](https://github.com/openai/gym) so that it presents a standard RL environment to the agent. We execute Distiller's sample application [amc.py](https://github.com/NervanaSystems/distiller/blob/new_amc/examples/auto_compression/amc/amc.py) with special arguments telling it how to invoke AMC. This creates a DistillerWrapperEnvironment environment and a DDPG agent and starts the training cycle. For the exact technical details of doing this yourself, see the notebooks (better instructions will be added in the future).
+
+We thank Prof. Song Han and his team for their [help](https://github.com/mit-han-lab/amc-compressed-models) with certain critical parts of this implementation. However, all bugs in interpretation and/or implementation are ours ;-).
+
+[1] AMC: AutoML for Model Compression and Acceleration on Mobile Devices.<br>
+ Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han.<br>
+ In Proceedings of the European Conference on Computer Vision (ECCV), 2018.<br>
+ [arXiv:1802.03494](https://arxiv.org/abs/1802.03494)<br>
\ No newline at end of file
diff --git a/examples/auto_compression/amc/__init__.py b/examples/auto_compression/amc/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/automated_deep_compression/adc_vgg16_cifar_finetuning.yaml b/examples/auto_compression/amc/adc_vgg16_cifar_finetuning.yaml
similarity index 100%
rename from examples/automated_deep_compression/adc_vgg16_cifar_finetuning.yaml
rename to examples/auto_compression/amc/adc_vgg16_cifar_finetuning.yaml
diff --git a/examples/auto_compression/amc/amc.py b/examples/auto_compression/amc/amc.py
new file mode 100755
index 0000000..684277d
--- /dev/null
+++ b/examples/auto_compression/amc/amc.py
@@ -0,0 +1,216 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+$ python3 amc.py --arch=resnet20_cifar ${CIFAR10_PATH} --resume=../../ssl/checkpoints/checkpoint_trained_dense.pth.tar --amc --amc-procol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=DDPG --amc-cfg=auto_compression_channels.yaml --amc-rllib=private -j=1
+"""
+
+
+import math
+import os
+import copy
+import logging
+import numpy as np
+import torch
+import csv
+import traceback
+from functools import partial
+try:
+ import gym
+except ImportError as e:
+ print("WARNING: to use automated compression you will need to install extra packages")
+ print("See instructions in the interface of each RL library.")
+ raise e
+from gym import spaces
+import distiller
+from collections import OrderedDict, namedtuple
+from types import SimpleNamespace
+from distiller import normalize_module_name, SummaryGraph
+from environment import DistillerWrapperEnvironment, Observation
+from utils.features_collector import collect_intermediate_featuremap_samples
+import distiller.apputils as apputils
+import distiller.apputils.image_classifier as classifier
+from rewards import reward_factory
+
+
+msglogger = logging.getLogger()
+
+
+class AutoCompressionSampleApp(classifier.ClassifierCompressor):
+ def __init__(self, args, script_dir):
+ super().__init__(args, script_dir)
+
+ def train_auto_compressor(self):
+ using_fm_reconstruction = self.args.amc_prune_method == 'fm-reconstruction'
+ fixed_subset, sequential = (using_fm_reconstruction, using_fm_reconstruction)
+ msglogger.info("ADC: fixed_subset=%s\tsequential=%s" % (fixed_subset, sequential))
+ train_loader, val_loader, test_loader = classifier.load_data(self.args, fixed_subset, sequential)
+
+ self.args.display_confusion = False
+ validate_fn = partial(classifier.test, test_loader=val_loader, criterion=self.criterion,
+ loggers=self.pylogger, args=self.args, activations_collectors=None)
+ train_fn = partial(classifier.train, train_loader=train_loader, criterion=self.criterion,
+ loggers=self.pylogger, args=self.args)
+
+ save_checkpoint_fn = partial(apputils.save_checkpoint, arch=self.args.arch, dir=msglogger.logdir)
+ optimizer_data = {'lr': self.args.lr, 'momentum': self.args.momentum, 'weight_decay': self.args.weight_decay}
+ return train_auto_compressor(self.model, self.args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)
+
+
+def main():
+ import amc_args
+ # Parse arguments
+ args = classifier.init_classifier_compression_arg_parser()
+ args = amc_args.add_automl_args(args).parse_args()
+ app = AutoCompressionSampleApp(args, script_dir=os.path.dirname(__file__))
+ return app.train_auto_compressor()
+
+
+def train_auto_compressor(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn):
+ dataset = args.dataset
+ arch = args.arch
+ num_ft_epochs = args.amc_ft_epochs
+ action_range = args.amc_action_range
+
+ config_verbose(False)
+
+ # Read the experiment configuration
+ amc_cfg_fname = args.amc_cfg_file
+ if not amc_cfg_fname:
+ raise ValueError("You must specify a valid configuration file path using --amc-cfg")
+
+ with open(amc_cfg_fname, 'r') as cfg_file:
+ compression_cfg = distiller.utils.yaml_ordered_load(cfg_file)
+
+ if not args.amc_rllib:
+ raise ValueError("You must set --amc-rllib to a valid value")
+
+ #rl_lib = compression_cfg["rl_lib"]["name"]
+ #msglogger.info("Executing AMC: RL agent - %s RL library - %s", args.amc_agent_algo, rl_lib)
+
+ # Create a dictionary of parameters that Coach will handover to DistillerWrapperEnvironment
+ # Once it creates it.
+ services = distiller.utils.MutableNamedTuple({
+ 'validate_fn': validate_fn,
+ 'save_checkpoint_fn': save_checkpoint_fn,
+ 'train_fn': train_fn})
+
+ app_args = distiller.utils.MutableNamedTuple({
+ 'dataset': dataset,
+ 'arch': arch,
+ 'optimizer_data': optimizer_data,
+ 'seed': args.seed})
+
+ ddpg_cfg = distiller.utils.MutableNamedTuple({
+ 'heatup_noise': 0.5,
+ 'initial_training_noise': 0.5,
+ 'training_noise_decay': 0.99555, #0.98, #0.996,
+ 'num_heatup_episodes': args.amc_heatup_episodes,
+ 'num_training_episodes': args.amc_training_episodes,
+ 'actor_lr': 1e-4,
+ 'critic_lr': 1e-3})
+
+ amc_cfg = distiller.utils.MutableNamedTuple({
+ 'modules_dict': compression_cfg["network"], # dict of modules, indexed by arch name
+ 'save_chkpts': args.amc_save_chkpts,
+ 'protocol': args.amc_protocol,
+ 'agent_algo': args.amc_agent_algo,
+ 'num_ft_epochs': num_ft_epochs,
+ 'action_range': action_range,
+ 'reward_frequency': args.amc_reward_frequency,
+ 'ft_frequency': args.amc_ft_frequency,
+ 'pruning_pattern': args.amc_prune_pattern,
+ 'pruning_method': args.amc_prune_method,
+ 'group_size': args.amc_group_size,
+ 'n_points_per_fm': args.amc_fm_reconstruction_n_pts,
+ 'ddpg_cfg': ddpg_cfg,
+ 'ranking_noise': args.amc_ranking_noise})
+
+ #net_wrapper = NetworkWrapper(model, app_args, services)
+ #return sample_networks(net_wrapper, services)
+
+ amc_cfg.target_density = args.amc_target_density
+ amc_cfg.reward_fn, amc_cfg.action_constrain_fn = reward_factory(args.amc_protocol)
+
+ def create_environment():
+ env = DistillerWrapperEnvironment(model, app_args, amc_cfg, services)
+ env.amc_cfg.ddpg_cfg.replay_buffer_size = 100 * env.steps_per_episode
+ return env
+
+ env1 = create_environment()
+
+ if args.amc_rllib == "spinningup":
+ from rl_libs.spinningup import spinningup_if
+ rl = spinningup_if.RlLibInterface()
+ env2 = create_environment()
+ steps_per_episode = env1.steps_per_episode
+ rl.solve(env1, env2)
+ elif args.amc_rllib == "private":
+ from rl_libs.private import private_if
+ rl = private_if.RlLibInterface()
+ args.observation_len = len(Observation._fields)
+ rl.solve(env1, args)
+ elif args.amc_rllib == "coach":
+ from rl_libs.coach import coach_if
+ rl = coach_if.RlLibInterface()
+ env_cfg = {'model': model,
+ 'app_args': app_args,
+ 'amc_cfg': amc_cfg,
+ 'services': services}
+ steps_per_episode = env1.steps_per_episode
+ rl.solve(**env_cfg, steps_per_episode=steps_per_episode)
+ elif args.amc_rllib == "random":
+ from rl_libs.random import random_if
+ rl = random_if.RlLibInterface()
+ return rl.solve(env1)
+ else:
+ raise ValueError("unsupported rl library: ", args.amc_rllib)
+
+
+def config_verbose(verbose):
+ if verbose:
+ loglevel = logging.DEBUG
+ else:
+ loglevel = logging.INFO
+ logging.getLogger().setLevel(logging.WARNING)
+ for module in ["examples.auto_compression.amc",
+ "distiller.apputils.image_classifier",
+ "distiller.data_loggers.logger",
+ "distiller.thinning",
+ "distiller.pruning.ranked_structures_pruner"]:
+ logging.getLogger(module).setLevel(loglevel)
+
+
+if __name__ == '__main__':
+ try:
+ main()
+ except KeyboardInterrupt:
+ print("\n-- KeyboardInterrupt --")
+ except Exception as e:
+ if msglogger is not None:
+ # We catch unhandled exceptions here in order to log them to the log file
+ # However, using the msglogger as-is to do that means we get the trace twice in stdout - once from the
+ # logging operation and once from re-raising the exception. So we remove the stdout logging handler
+ # before logging the exception
+ handlers_bak = msglogger.handlers
+ msglogger.handlers = [h for h in msglogger.handlers if type(h) != logging.StreamHandler]
+ msglogger.error(traceback.format_exc())
+ msglogger.handlers = handlers_bak
+ raise
+ finally:
+ if msglogger is not None and hasattr(msglogger, 'log_filename'):
+ msglogger.info('')
+ msglogger.info('Log file for this run: ' + os.path.realpath(msglogger.log_filename))
\ No newline at end of file
diff --git a/examples/automated_deep_compression/automl_args.py b/examples/auto_compression/amc/amc_args.py
similarity index 52%
rename from examples/automated_deep_compression/automl_args.py
rename to examples/auto_compression/amc/amc_args.py
index 80b732f..3d4edd6 100755
--- a/examples/automated_deep_compression/automl_args.py
+++ b/examples/auto_compression/amc/amc_args.py
@@ -5,12 +5,14 @@ def add_automl_args(argparser, arch_choices=None, enable_pretrained=False):
Arguments:
argparser (argparse.ArgumentParser): Existing parser to which to add the arguments
"""
- argparser.add_argument('--amc', dest='AMC', action='store_true', help='AutoML Compression')
group = argparser.add_argument_group('AutoML Compression Arguments')
+ group.add_argument('--amc-cfg', dest='amc_cfg_file', type=str, action='store',
+ help='AMC configuration file')
group.add_argument('--amc-protocol', choices=["mac-constrained",
- "param-constrained",
+ #"param-constrained",
"accuracy-guaranteed",
- "mac-constrained-experimental"],
+ "mac-constrained-experimental",
+ "punish-agent"],
default="mac-constrained", help='Compression-policy search protocol')
group.add_argument('--amc-ft-epochs', type=int, default=1,
help='The number of epochs to fine-tune each discovered network')
@@ -18,22 +20,39 @@ def add_automl_args(argparser, arch_choices=None, enable_pretrained=False):
help='Save checkpoints of all discovered networks')
group.add_argument('--amc-action-range', type=float, nargs=2, default=[0.0, 0.80],
help='Density action range (a_min, a_max)')
- group.add_argument('--amc-heatup-epochs', type=int, default=100,
- help='The number of epochs for heatup/exploration')
- group.add_argument('--amc-training-epochs', type=int, default=300,
- help='The number of epochs for training/exploitation')
+ group.add_argument('--amc-heatup-episodes', type=int, default=100,
+ help='The number of episodes for heatup/exploration')
+ group.add_argument('--amc-training-episodes', type=int, default=700,
+ help='The number of episodes for training/exploitation')
group.add_argument('--amc-reward-frequency', type=int, default=None,
help='Reward computation frequency (measured in agent steps)')
group.add_argument('--amc-target-density', type=float,
help='Target density of the network we are seeking')
group.add_argument('--amc-agent-algo', choices=["ClippedPPO-continuous",
"ClippedPPO-discrete",
+ "TD3",
"DDPG",
"Random-policy"],
default="ClippedPPO-continuous",
help="The agent algorithm to use")
- # group.add_argument('--amc-thinning', action='store_true', default=False,
- # help='Perform netowrk thinning after altering each layer')
group.add_argument('--amc-ft-frequency', type=int, default=None,
help='How many action-steps between fine-tuning.\n'
'By default there is no fine-tuning between steps.')
+ group.add_argument('--amc-prune-pattern', choices=["filters", "channels"],
+ default="filters", help="The pruning pattern")
+ group.add_argument('--amc-prune-method', choices=["l1-rank",
+ "stochastic-l1-rank",
+ "fm-reconstruction"],
+ default="l1-rank", help="The pruning method")
+ group.add_argument('--amc-rllib', choices=["coach",
+ "spinningup",
+ "private",
+ "random"],
+ default=None, help="Choose which RL library to use")
+ group.add_argument('--amc-group-size', type=int, default=1,
+ help="Number of filters/channels to group")
+ group.add_argument('--amc-reconstruct-pts', dest="amc_fm_reconstruction_n_pts", type=int, default=10,
+ help="Number of filters/channels to group")
+ group.add_argument('--amc-ranking-noise', type=float, default=0.,
+ help='Strcuture ranking noise')
+ return argparser
\ No newline at end of file
diff --git a/examples/auto_compression/amc/auto_compression_channels.yaml b/examples/auto_compression/amc/auto_compression_channels.yaml
new file mode 100755
index 0000000..a58493d
--- /dev/null
+++ b/examples/auto_compression/amc/auto_compression_channels.yaml
@@ -0,0 +1,100 @@
+# rl_lib:
+# # Choose which RL library to use: Coach from Intel AI Lab, or Spinup from OpenAI
+# name: private # one of: coach, spinningup, private
+
+network:
+ mobilenet:
+ # Only conv 1x1
+ ["module.model.1.3", "module.model.2.3",
+ "module.model.3.3", "module.model.4.3", "module.model.5.3",
+ "module.model.6.3", "module.model.7.3", "module.model.8.3",
+ "module.model.9.3", "module.model.10.3", "module.model.11.3",
+ "module.model.12.3", "module.model.13.3"]
+
+ mobilenet_v2:
+ # Only conv 1x1, without shortcut connection dependencies
+ [
+ "module.features.1.conv.1",
+ "module.features.2.conv.0.0", "module.features.2.conv.2",
+ # "module.features.3.conv.0.0",
+ "module.features.3.conv.2",
+ # "module.features.4.conv.0.0",
+ "module.features.4.conv.2",
+ # "module.features.5.conv.0.0",
+ "module.features.5.conv.2",
+ # "module.features.6.conv.0.0",
+ "module.features.6.conv.2",
+ # "module.features.7.conv.0.0",
+ "module.features.7.conv.2",
+ # "module.features.8.conv.0.0",
+ "module.features.8.conv.2",
+ # "module.features.9.conv.0.0",
+ "module.features.9.conv.2",
+ # "module.features.10.conv.0.0",
+ "module.features.10.conv.2",
+ # "module.features.11.conv.0.0",
+ "module.features.11.conv.2",
+ # "module.features.12.conv.0.0",
+ "module.features.12.conv.2",
+ # "module.features.13.conv.0.0",
+ "module.features.13.conv.2",
+ # "module.features.14.conv.0.0",
+ "module.features.14.conv.2",
+ # "module.features.15.conv.0.0",
+ "module.features.15.conv.2",
+ # "module.features.16.conv.0.0",
+ "module.features.16.conv.2",
+ #"module.features.17.conv.0.0",
+ "module.features.17.conv.2",
+ "module.features.18.0"
+ ]
+
+ resnet50:
+ ["module.layer1.0.conv3", "module.layer1.0.conv2",
+ "module.layer1.1.conv3", "module.layer1.1.conv2",
+ "module.layer1.2.conv3", "module.layer1.2.conv2",
+ "module.layer2.0.conv3", "module.layer2.0.conv2",
+ "module.layer2.1.conv3", "module.layer2.1.conv2",
+ "module.layer2.2.conv3", "module.layer2.2.conv2",
+ "module.layer2.3.conv3", "module.layer2.3.conv2",
+ "module.layer3.0.conv3", "module.layer3.0.conv2",
+ "module.layer3.1.conv3", "module.layer3.1.conv2",
+ "module.layer3.2.conv3", "module.layer3.2.conv2",
+ "module.layer3.3.conv3", "module.layer3.3.conv2",
+ "module.layer3.4.conv3", "module.layer3.4.conv2",
+ "module.layer3.5.conv3", "module.layer3.5.conv2",
+ "module.layer4.0.conv3", "module.layer4.0.conv2",
+ "module.layer4.1.conv3", "module.layer4.1.conv2",
+ "module.layer4.2.conv3", "module.layer4.2.conv2"]
+
+ resnet20_cifar:
+ ["module.layer1.0.conv2", "module.layer1.1.conv2", "module.layer1.2.conv2",
+ "module.layer2.0.conv2", "module.layer2.1.conv2", "module.layer2.2.conv2",
+ "module.layer3.0.conv2", "module.layer3.1.conv2", "module.layer3.2.conv2"]
+
+ resnet56_cifar:
+ ["module.layer1.0.conv2", "module.layer1.1.conv2", "module.layer1.2.conv2",
+ "module.layer1.3.conv2", "module.layer1.4.conv2", "module.layer1.5.conv2",
+ "module.layer1.6.conv2", "module.layer1.7.conv2", "module.layer1.8.conv2",
+
+ "module.layer2.0.conv2", "module.layer2.1.conv2", "module.layer2.2.conv2",
+ "module.layer2.3.conv2", "module.layer2.4.conv2", "module.layer2.5.conv2",
+ "module.layer2.6.conv2", "module.layer2.7.conv2", "module.layer2.8.conv2",
+
+ "module.layer3.0.conv2", "module.layer3.1.conv2", "module.layer3.2.conv2",
+ "module.layer3.3.conv2", "module.layer3.4.conv2", "module.layer3.5.conv2",
+ "module.layer3.6.conv2", "module.layer3.7.conv2", "module.layer3.8.conv2"]
+
+ plain20_cifar:
+ ["module.layer1.0.conv1", "module.layer1.0.conv2",
+ "module.layer1.1.conv1", "module.layer1.1.conv2",
+ "module.layer1.2.conv1", "module.layer1.2.conv2",
+ "module.layer2.0.conv1", "module.layer2.0.conv2",
+ "module.layer2.1.conv1", "module.layer2.1.conv2",
+ "module.layer2.2.conv1", "module.layer2.2.conv2",
+ "module.layer3.0.conv1", "module.layer3.0.conv2",
+ "module.layer3.1.conv1", "module.layer3.1.conv2",
+ "module.layer3.2.conv1", "module.layer3.2.conv2"]
+
+ simplenet_mnist:
+ ["module.conv2"]
diff --git a/examples/auto_compression/amc/auto_compression_filters.yaml b/examples/auto_compression/amc/auto_compression_filters.yaml
new file mode 100755
index 0000000..c80d59c
--- /dev/null
+++ b/examples/auto_compression/amc/auto_compression_filters.yaml
@@ -0,0 +1,58 @@
+# rl_lib:
+# # Choose which RL library to use: Coach from Intel AI Lab, or Spinup from OpenAI
+# name: private # one of: coach, spinningup, private
+
+network:
+ mobilenet:
+ [#"module.model.0.0",
+ "module.model.1.3", "module.model.2.3",
+ "module.model.3.3", "module.model.4.3", "module.model.5.3",
+ "module.model.6.3", "module.model.7.3", "module.model.8.3",
+ "module.model.9.3", "module.model.10.3", "module.model.11.3",
+ "module.model.12.3", "module.model.13.3"]
+ resnet50:
+ ["module.layer1.0.conv1", "module.layer1.0.conv2",
+ "module.layer1.1.conv1", "module.layer1.1.conv2",
+ "module.layer1.2.conv1", "module.layer1.2.conv2",
+ "module.layer2.0.conv1", "module.layer2.0.conv2",
+ "module.layer2.1.conv1", "module.layer2.1.conv2",
+ "module.layer2.2.conv1", "module.layer2.2.conv2",
+ "module.layer2.3.conv1", "module.layer2.3.conv2",
+ "module.layer3.0.conv1", "module.layer3.0.conv2",
+ "module.layer3.1.conv1", "module.layer3.1.conv2",
+ "module.layer3.2.conv1", "module.layer3.2.conv2",
+ "module.layer3.3.conv1", "module.layer3.3.conv2",
+ "module.layer3.4.conv1", "module.layer3.4.conv2",
+ "module.layer3.5.conv1", "module.layer3.5.conv2",
+ "module.layer4.0.conv1", "module.layer4.0.conv2",
+ "module.layer4.1.conv1", "module.layer4.1.conv2",
+ "module.layer4.2.conv1", "module.layer4.2.conv2"]
+ resnet20_cifar:
+ ["module.layer1.0.conv1", "module.layer1.1.conv1", "module.layer1.2.conv1",
+ "module.layer2.0.conv1", "module.layer2.1.conv1", "module.layer2.2.conv1",
+ "module.layer3.0.conv1", "module.layer3.1.conv1", "module.layer3.2.conv1"]
+ resnet56_cifar:
+ [ "module.layer1.0.conv1", "module.layer1.1.conv1", "module.layer1.2.conv1",
+ "module.layer1.3.conv1", "module.layer1.4.conv1", "module.layer1.5.conv1",
+ "module.layer1.6.conv1", "module.layer1.7.conv1", "module.layer1.8.conv1",
+
+ "module.layer2.0.conv1", "module.layer2.1.conv1", "module.layer2.2.conv1",
+ "module.layer2.3.conv1", "module.layer2.4.conv1", "module.layer2.5.conv1",
+ "module.layer2.6.conv1", "module.layer2.7.conv1", "module.layer2.8.conv1",
+
+ "module.layer3.0.conv1", "module.layer3.1.conv1", "module.layer3.2.conv1",
+ "module.layer3.3.conv1", "module.layer3.4.conv1", "module.layer3.5.conv1",
+ "module.layer3.6.conv1", "module.layer3.7.conv1", "module.layer3.8.conv1"]
+ plain20_cifar:
+ [
+ "module.layer1.0.conv1",
+ "module.layer1.0.conv2",
+ "module.layer1.1.conv1", "module.layer1.1.conv2",
+ "module.layer1.2.conv1", "module.layer1.2.conv2",
+ "module.layer2.0.conv1", "module.layer2.0.conv2",
+ "module.layer2.1.conv1", "module.layer2.1.conv2",
+ "module.layer2.2.conv1", "module.layer2.2.conv2",
+ "module.layer3.0.conv1", "module.layer3.0.conv2",
+ "module.layer3.1.conv1", "module.layer3.1.conv2",
+ "module.layer3.2.conv1", "module.layer3.2.conv2"
+ ]
diff --git a/examples/auto_compression/amc/environment.py b/examples/auto_compression/amc/environment.py
new file mode 100755
index 0000000..af6a5e7
--- /dev/null
+++ b/examples/auto_compression/amc/environment.py
@@ -0,0 +1,823 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+We thank Prof. Song Han and his team for their help with certain critical parts
+of this implementation.
+
+AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
+ Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han
+ arXiv:1802.03494
+"""
+
+import math
+import os
+import copy
+import logging
+import numpy as np
+import torch
+try:
+ import gym
+except ImportError as e:
+ print("WARNING: to use automated compression you will need to install extra packages")
+ print("See instructions in the header of examples/automated_deep_compression/ADC.py")
+ raise e
+from gym import spaces
+import distiller
+from collections import OrderedDict, namedtuple
+from types import SimpleNamespace
+from distiller import normalize_module_name, SummaryGraph
+from utils.features_collector import collect_intermediate_featuremap_samples
+from utils.ac_loggers import AMCStatsLogger, FineTuneStatsLogger
+
+
+msglogger = logging.getLogger("examples.auto_compression.amc")
+Observation = namedtuple('Observation', ['t', 'n', 'c', 'h', 'w', 'stride', 'k', 'MACs',
+ 'Weights', 'reduced', 'rest', 'prev_a'])
+ObservationLen = len(Observation._fields)
+ALMOST_ONE = 0.9999
+
+
+def is_using_continuous_action_space(agent):
+ return agent in ("DDPG", "ClippedPPO-continuous", "Random-policy", "TD3")
+
+
+def log_amc_config(amc_cfg):
+ try:
+ msglogger.info('AMC configuration:')
+ for k, v in amc_cfg.items():
+ msglogger.info("\t{} : {}".format(k, v))
+ except TypeError as e:
+ pass
+
+
+# Work around PPO's Gaussian sampling space
+# See: https://github.com/openai/baselines/issues/121
+PPO_MIN, PPO_MAX = -3, 3 # These are empiric values that are a very rough estimate
+def adjust_ppo_output(ppo_pruning_action, action_high, action_low):
+ """
+ We need to map PPO's infinite action-space to our action-space (in PPO actions are sampled
+ from a normalized Gaussian N(0,1)).
+ We do this by a shift & scale operation:
+ 1. We crudely assume that most of the Gaussian maps to actions in a limited range {PPO_MIN..PPO_MAX}
+ which allows us to clip the action, and scale it to the range of the actual action-space.
+ 2. We assume that the distribution the agent samples from is centered at zero, and so we shift
+ actions to the center of the actual action-space.
+ """
+ shift = (action_high - action_low) / 2 + action_low
+ scale = (action_high - action_low) / (PPO_MAX - PPO_MIN)
+ pruning_action = ppo_pruning_action * scale + shift
+ return float(pruning_action)
+
+
+class NetworkMetadata(object):
+ def __init__(self, model, dataset, dependency_type, modules_list):
+ details = get_network_details(model, dataset, dependency_type, modules_list)
+ self.all_layers, self.pruned_idxs, self.dependent_idxs, self._total_macs, self._total_nnz = details
+
+ @property
+ def total_macs(self):
+ return self._total_macs
+
+ @property
+ def total_nnz(self):
+ return self._total_nnz
+
+ def layer_net_macs(self, layer):
+ """Returns a MACs of a specific layer"""
+ return layer.macs
+
+ def layer_macs(self, layer):
+ """Returns a MACs of a specific layer, with the impact on pruning-dependent layers"""
+ macs = layer.macs
+ for dependent_mod in layer.dependencies:
+ macs += self.name2layer(dependent_mod).macs
+ return macs
+
+ def reduce_layer_macs(self, layer, reduction):
+ total_macs_reduced = layer.macs * reduction
+ total_nnz_reduced = layer.weights_vol * reduction
+ layer.macs -= total_macs_reduced
+ layer.weights_vol -= total_nnz_reduced
+ for dependent_mod in layer.dependencies:
+ macs_reduced = self.name2layer(dependent_mod).macs * reduction
+ nnz_reduced = self.name2layer(dependent_mod).weights_vol * reduction
+ total_macs_reduced += macs_reduced
+ total_nnz_reduced += nnz_reduced
+ self.name2layer(dependent_mod).macs -= macs_reduced
+ self.name2layer(dependent_mod).weights_vol -= nnz_reduced
+ self._total_macs -= total_macs_reduced
+ self._total_nnz -= total_nnz_reduced
+
+ def name2layer(self, name):
+ layers = [layer for layer in self.all_layers.values() if layer.name == name]
+ if len(layers) == 1:
+ return layers[0]
+ raise ValueError("illegal module name %s" % name)
+
+ def model_budget(self):
+ return self._total_macs, self._total_nnz
+
+ def get_layer(self, layer_id):
+ return self.all_layers[layer_id]
+
+ def get_pruned_layer(self, layer_id):
+ assert self.is_prunable(layer_id)
+ return self.get_layer(layer_id)
+
+ def is_prunable(self, layer_id):
+ return layer_id in self.pruned_idxs
+
+ def is_reducible(self, layer_id):
+ return layer_id in self.pruned_idxs or layer_id in self.dependent_idxs
+
+ def num_pruned_layers(self):
+ return len(self.pruned_idxs)
+
+ def num_layers(self):
+ return len(self.all_layers)
+
+ def performance_summary(self):
+ # return OrderedDict({layer.name: (layer.macs, layer.weights_vol)
+ # for layer in self.all_layers.values()})
+ return OrderedDict({layer.name: layer.macs
+ for layer in self.all_layers.values()})
+
+
+class NetworkWrapper(object):
+ def __init__(self, model, app_args, services, modules_list, pruning_pattern):
+ self.app_args = app_args
+ self.services = services
+ self.cached_model_metadata = NetworkMetadata(model, app_args.dataset,
+ pruning_pattern, modules_list)
+ self.cached_perf_summary = self.cached_model_metadata.performance_summary()
+ self.reset(model)
+
+ def reset(self, model):
+ self.model = model
+ self.zeros_mask_dict = distiller.create_model_masks_dict(self.model)
+ self.model_metadata = copy.deepcopy(self.cached_model_metadata)
+
+ def get_resources_requirements(self):
+ total_macs, total_nnz = self.model_metadata.model_budget()
+ return total_macs, total_nnz
+
+ @property
+ def arch(self):
+ return self.app_args.arch
+
+ def num_pruned_layers(self):
+ return self.model_metadata.num_pruned_layers()
+
+ def get_pruned_layer(self, layer_id):
+ return self.model_metadata.get_pruned_layer(layer_id)
+
+ def get_layer(self, idx):
+ return self.model_metadata.get_layer(idx)
+
+ def layer_macs(self, layer):
+ return self.model_metadata.layer_macs(layer)
+
+ def layer_net_macs(self, layer):
+ return self.model_metadata.layer_net_macs(layer)
+
+ def name2layer(self, name):
+ return self.model_metadata.name2layer(name)
+
+ @property
+ def total_macs(self):
+ return self.model_metadata.total_macs
+
+ @property
+ def total_nnz(self):
+ return self.model_metadata.total_nnz
+
+ def performance_summary(self):
+ """Return a dictionary representing the performance the model.
+
+ We calculate the performance of each layer relative to the original (uncompressed) model.
+ """
+ current_perf = self.model_metadata.performance_summary()
+ ret = OrderedDict()
+ #return OrderedDict({k: v/v_baseline for ((k, v), (v_baseline)) in zip(current_perf.items(), self.cached_perf_summary.values())})
+ for k, v in current_perf.items():
+ ret[k] = v / self.cached_perf_summary[k]
+ return ret
+
+ def create_scheduler(self):
+ scheduler = distiller.CompressionScheduler(self.model)
+ masks = {param_name: masker.mask for param_name, masker in self.zeros_mask_dict.items()}
+ scheduler.load_state_dict(state={'masks_dict': masks})
+ return scheduler
+
+ def remove_structures(self, layer_id, fraction_to_prune, prune_what, prune_how,
+ group_size, apply_thinning, ranking_noise):
+ """Physically remove channels and corresponding filters from the model
+
+ Returns the compute-sparsity of the layer with index 'layer_id'
+ """
+ if layer_id not in self.model_metadata.pruned_idxs:
+ raise ValueError("idx=%d is not in correct range " % layer_id)
+ if fraction_to_prune < 0:
+ raise ValueError("fraction_to_prune=%.3f is illegal" % fraction_to_prune)
+
+ if fraction_to_prune == 0:
+ return 0
+ if fraction_to_prune == 1.0:
+ # For now, prevent the removal of entire layers
+ fraction_to_prune = ALMOST_ONE
+
+ layer = self.model_metadata.get_pruned_layer(layer_id)
+ macs_before = self.layer_net_macs(layer)
+ conv_pname = layer.name + ".weight"
+ conv_p = distiller.model_find_param(self.model, conv_pname)
+
+ msglogger.debug("ADC: trying to remove %.1f%% %s from %s" % (fraction_to_prune*100, prune_what, conv_pname))
+
+ if prune_what == "channels":
+ calculate_sparsity = distiller.sparsity_ch
+ remove_structures_fn = distiller.remove_channels
+ group_type = "Channels"
+ elif prune_what == "filters":
+ calculate_sparsity = distiller.sparsity_3D
+ group_type = "Filters"
+ remove_structures_fn = distiller.remove_filters
+ else:
+ raise ValueError("unsupported structure {}".format(prune_what))
+
+ if prune_how == "l1-rank" or prune_how == "stochastic-l1-rank":
+ # Create a channel/filter-ranking pruner
+ pruner = distiller.pruning.L1RankedStructureParameterPruner(
+ "auto_pruner", group_type, fraction_to_prune, conv_pname,
+ noise=ranking_noise, group_size=group_size)
+ meta = None
+ elif prune_how == "fm-reconstruction":
+ pruner = distiller.pruning.FMReconstructionChannelPruner(
+ "auto_pruner", group_type, fraction_to_prune, conv_pname,
+ group_size, math.ceil, ranking_noise=ranking_noise)
+ meta = {'model': self.model}
+ else:
+ raise ValueError("Unknown pruning method")
+ pruner.set_param_mask(conv_p, conv_pname, self.zeros_mask_dict, meta=meta)
+ del pruner
+
+ if (self.zeros_mask_dict[conv_pname].mask is None or
+ 0 == calculate_sparsity(self.zeros_mask_dict[conv_pname].mask)):
+ msglogger.debug("remove_structures: aborting because there are no structures to prune")
+ return 0
+ final_action = calculate_sparsity(self.zeros_mask_dict[conv_pname].mask)
+
+ # Use the mask to prune
+ self.zeros_mask_dict[conv_pname].apply_mask(conv_p)
+ if apply_thinning:
+ remove_structures_fn(self.model, self.zeros_mask_dict, self.app_args.arch, self.app_args.dataset, optimizer=None)
+
+ self.model_metadata.reduce_layer_macs(layer, final_action)
+ macs_after = self.layer_net_macs(layer)
+ assert 1. - (macs_after / macs_before) == final_action
+ return final_action
+
+ def validate(self):
+ top1, top5, vloss = self.services.validate_fn(model=self.model)
+ return top1, top5, vloss
+
+ def train(self, num_epochs, episode=0):
+ # Train for zero or more epochs
+ opt_cfg = self.app_args.optimizer_data
+ optimizer = torch.optim.SGD(self.model.parameters(), lr=opt_cfg['lr'],
+ momentum=opt_cfg['momentum'], weight_decay=opt_cfg['weight_decay'])
+ compression_scheduler = self.create_scheduler()
+ acc_list = []
+ for _ in range(num_epochs):
+ # Fine-tune the model
+ accuracies = self.services.train_fn(model=self.model, compression_scheduler=compression_scheduler,
+ optimizer=optimizer, epoch=episode)
+ acc_list.extend(accuracies)
+ del compression_scheduler
+ return acc_list
+
+
+class DistillerWrapperEnvironment(gym.Env):
+ def __init__(self, model, app_args, amc_cfg, services):
+ self.pylogger = distiller.data_loggers.PythonLogger(msglogger)
+ logdir = logging.getLogger().logdir
+ self.tflogger = distiller.data_loggers.TensorBoardLogger(logdir)
+ self.verbose = False
+ self.orig_model = copy.deepcopy(model)
+ self.app_args = app_args
+ self.amc_cfg = amc_cfg
+ self.services = services
+
+ try:
+ modules_list = amc_cfg.modules_dict[app_args.arch]
+ except KeyError:
+ msglogger.warning("!!! The config file does not specify the modules to compress for %s" % app_args.arch)
+ # Default to using all convolution layers
+ distiller.assign_layer_fq_names(model)
+ modules_list = [mod.distiller_name for mod in model.modules() if type(mod)==torch.nn.Conv2d]
+ msglogger.warning("Using the following layers: %s" % ", ".join(modules_list))
+
+ self.net_wrapper = NetworkWrapper(model, app_args, services, modules_list, amc_cfg.pruning_pattern)
+ self.original_model_macs, self.original_model_size = self.net_wrapper.get_resources_requirements()
+ self.reset(init_only=True)
+ msglogger.debug("Model %s has %d modules (%d pruned)", self.app_args.arch,
+ self.net_wrapper.model_metadata.num_layers(),
+ self.net_wrapper.model_metadata.num_pruned_layers())
+ msglogger.debug("\tTotal MACs: %s" % distiller.pretty_int(self.original_model_macs))
+ msglogger.debug("\tTotal weights: %s" % distiller.pretty_int(self.original_model_size))
+ self._max_episode_steps = self.net_wrapper.model_metadata.num_pruned_layers() # Hack for Coach-TD3
+ log_amc_config(amc_cfg)
+
+ self.episode = 0
+ self.best_reward = float("-inf")
+ self.action_low = amc_cfg.action_range[0]
+ self.action_high = amc_cfg.action_range[1]
+
+ if is_using_continuous_action_space(self.amc_cfg.agent_algo):
+ if self.amc_cfg.agent_algo == "ClippedPPO-continuous":
+ self.action_space = spaces.Box(PPO_MIN, PPO_MAX, shape=(1,))
+ else:
+ self.action_space = spaces.Box(self.action_low, self.action_high, shape=(1,))
+ self.action_space.default_action = self.action_low
+ else:
+ self.action_space = spaces.Discrete(10)
+ self.observation_space = spaces.Box(0, float("inf"), shape=(len(Observation._fields),))
+ self.stats_logger = AMCStatsLogger(os.path.join(logdir, 'amc.csv'))
+ self.ft_stats_logger = FineTuneStatsLogger(os.path.join(logdir, 'ft_top1.csv'))
+
+ if self.amc_cfg.pruning_method == "fm-reconstruction":
+ if self.amc_cfg.pruning_pattern != "channels":
+ raise ValueError("Feature-map reconstruction is only supported when pruning weights channels")
+
+ from functools import partial
+ def acceptance_criterion(m, mod_names):
+ # Collect feature-maps only for Conv2d layers, if they are in our modules list.
+ return isinstance(m, torch.nn.Conv2d) and m.distiller_name in mod_names
+
+ # For feature-map reconstruction we need to collect a representative set
+ # of inter-layer feature-maps
+ from distiller.pruning import FMReconstructionChannelPruner
+ collect_intermediate_featuremap_samples(
+ self.net_wrapper.model,
+ self.net_wrapper.validate,
+ partial(acceptance_criterion, mod_names=modules_list),
+ partial(FMReconstructionChannelPruner.cache_featuremaps_fwd_hook,
+ n_points_per_fm=self.amc_cfg.n_points_per_fm))
+ @property
+ def steps_per_episode(self):
+ return self.net_wrapper.model_metadata.num_pruned_layers()
+
+ def reset(self, init_only=False):
+ """Reset the environment.
+ This is invoked by the Agent.
+ """
+ msglogger.info("Resetting the environment (init_only={})".format(init_only))
+ self.current_state_id = 0
+ self.current_layer_id = self.net_wrapper.model_metadata.pruned_idxs[self.current_state_id]
+ self.prev_action = 0
+ self.model = copy.deepcopy(self.orig_model)
+ if hasattr(self.net_wrapper.model, 'intermediate_fms'):
+ self.model.intermediate_fms = self.net_wrapper.model.intermediate_fms
+ self.net_wrapper.reset(self.model)
+ self._removed_macs = 0
+ self.action_history = []
+ self.agent_action_history = []
+ self.model_representation = self.get_model_representation()
+ if init_only:
+ return
+ initial_observation = self.get_obs()
+ return initial_observation
+
+ def current_layer(self):
+ return self.net_wrapper.get_pruned_layer(self.current_layer_id)
+
+ def episode_is_done(self):
+ return self.current_state_id == self.net_wrapper.model_metadata.num_pruned_layers()
+
+ @property
+ def removed_macs_pct(self):
+ """Return the amount of MACs removed so far.
+ This is normalized to the range 0..1
+ """
+ return self._removed_macs / self.original_model_macs
+
+ def render(self, mode='human'):
+ """Provide some feedback to the user about what's going on.
+ This is invoked by the Agent.
+ """
+ if self.current_state_id == 0:
+ msglogger.info("+" + "-" * 50 + "+")
+ msglogger.info("Starting a new episode %d", self.episode)
+ msglogger.info("+" + "-" * 50 + "+")
+ if not self.verbose:
+ return
+ msglogger.info("Render Environment: current_state_id=%d" % self.current_state_id)
+ distiller.log_weights_sparsity(self.model, -1, loggers=[self.pylogger])
+
+ def step(self, pruning_action):
+ """Take a step, given an action.
+
+ The action represents the desired sparsity for the "current" layer.
+ This function is invoked by the Agent.
+ """
+ pruning_action = float(pruning_action[0])
+ msglogger.debug("env.step - current_state_id=%d (%s) episode=%d action=%.2f" %
+ (self.current_state_id, self.current_layer().name, self.episode, pruning_action))
+ self.agent_action_history.append(pruning_action)
+
+ if is_using_continuous_action_space(self.amc_cfg.agent_algo):
+ if self.amc_cfg.agent_algo == "ClippedPPO-continuous":
+ # We need to map PPO's infinite action-space (actions sampled from a Gaussian) to our action-space.
+ pruning_action = adjust_ppo_output(pruning_action, self.action_high, self.action_low)
+ else:
+ pruning_action = np.clip(pruning_action, self.action_low, self.action_high)
+ else:
+ # Divide the action space into 10 discrete levels (0%, 10%, 20%,....90% sparsity)
+ pruning_action = pruning_action / 10
+ msglogger.debug("\tAgent clipped pruning_action={}".format(pruning_action))
+
+ if self.amc_cfg.action_constrain_fn is not None:
+ pruning_action = self.amc_cfg.action_constrain_fn(self, pruning_action=pruning_action)
+ msglogger.debug("Constrained pruning_action={}".format(pruning_action))
+
+ # Calculate the final compression rate
+ total_macs_before, _ = self.net_wrapper.get_resources_requirements()
+ layer_macs = self.net_wrapper.layer_macs(self.current_layer())
+ msglogger.debug("\tlayer_macs={:.2f}".format(layer_macs / self.original_model_macs))
+ msglogger.debug("\tremoved_macs={:.2f}".format(self.removed_macs_pct))
+ msglogger.debug("\trest_macs={:.2f}".format(self.rest_macs()))
+ msglogger.debug("\tcurrent_layer_id = %d" % self.current_layer_id)
+ self.current_state_id += 1
+ if pruning_action > 0:
+ pruning_action = self.net_wrapper.remove_structures(self.current_layer_id,
+ fraction_to_prune=pruning_action,
+ prune_what=self.amc_cfg.pruning_pattern,
+ prune_how=self.amc_cfg.pruning_method,
+ group_size=self.amc_cfg.group_size,
+ apply_thinning=self.episode_is_done(),
+ ranking_noise=self.amc_cfg.ranking_noise)
+ #random_state=self.random_state)
+ else:
+ pruning_action = 0
+
+ self.action_history.append(pruning_action)
+ total_macs_after_act, total_nnz_after_act = self.net_wrapper.get_resources_requirements()
+ layer_macs_after_action = self.net_wrapper.layer_macs(self.current_layer())
+
+ # Update the various counters after taking the step
+ self._removed_macs += (total_macs_before - total_macs_after_act)
+
+ msglogger.debug("\tactual_action={}".format(pruning_action))
+ msglogger.debug("\tlayer_macs={} layer_macs_after_action={} removed now={}".format(layer_macs,
+ layer_macs_after_action,
+ (layer_macs - layer_macs_after_action)))
+ msglogger.debug("\tself._removed_macs={}".format(self._removed_macs))
+ assert math.isclose(layer_macs_after_action / layer_macs, 1 - pruning_action)
+
+ stats = ('Performance/Validation/',
+ OrderedDict([('requested_action', pruning_action)]))
+
+ distiller.log_training_progress(stats, None,
+ self.episode, steps_completed=self.current_state_id,
+ total_steps=self.net_wrapper.num_pruned_layers(), log_freq=1, loggers=[self.tflogger])
+
+ if self.episode_is_done():
+ msglogger.info("Episode is ending")
+ observation = self.get_final_obs()
+ reward, top1 = self.compute_reward(total_macs_after_act, total_nnz_after_act)
+ normalized_macs = total_macs_after_act / self.original_model_macs * 100
+ normalized_nnz = total_nnz_after_act / self.original_model_size * 100
+ self.finalize_episode(top1, reward, total_macs_after_act, normalized_macs,
+ normalized_nnz, self.action_history, self.agent_action_history)
+ self.episode += 1
+ else:
+ self.current_layer_id = self.net_wrapper.model_metadata.pruned_idxs[self.current_state_id]
+
+ if self.amc_cfg.ft_frequency is not None and self.current_state_id % self.amc_cfg.ft_frequency == 0:
+ self.net_wrapper.train(1, self.episode)
+ observation = self.get_obs()
+ if self.amc_cfg.reward_frequency is not None and self.current_state_id % self.amc_cfg.reward_frequency == 0:
+ reward, top1 = self.compute_reward(total_macs_after_act, total_nnz_after_act, log_stats=False)
+ else:
+ reward = 0
+ self.prev_action = pruning_action
+ if self.episode_is_done():
+ info = {"accuracy": top1, "compress_ratio": normalized_macs}
+ msglogger.info(self.removed_macs_pct)
+ if self.amc_cfg.protocol == "mac-constrained":
+ # Sanity check (special case only for "mac-constrained")
+ #assert self.removed_macs_pct >= 1 - self.amc_cfg.target_density - 0.01
+ pass
+ else:
+ info = {}
+ return observation, reward, self.episode_is_done(), info
+
+ def get_obs(self):
+ """Produce a state embedding (i.e. an observation)"""
+ current_layer_macs = self.net_wrapper.layer_net_macs(self.current_layer())
+ current_layer_macs_pct = current_layer_macs/self.original_model_macs
+ current_layer = self.current_layer()
+ conv_module = distiller.model_find_module(self.model, current_layer.name)
+
+ obs = self.model_representation[self.current_state_id, :]
+ obs[-1] = self.prev_action
+ obs[-2] = self.rest_macs()
+ obs[-3] = self.removed_macs_pct
+ msglogger.debug("obs={}".format(Observation._make(obs)))
+ # Sanity check
+ assert (self.removed_macs_pct + current_layer_macs_pct + self.rest_macs()) <= 1
+ return obs
+
+ def get_final_obs(self):
+ """Return the final state embedding (observation).
+
+ The final state is reached after we traverse all of the Convolution layers.
+ """
+ obs = self.model_representation[-1, :]
+ msglogger.debug("obs={}".format(Observation._make(obs)))
+ return obs
+
+ def get_model_representation(self):
+ """Initialize an embedding representation of the entire model.
+
+ At runtime, a specific row in the embedding matrix is chosen (depending on
+ the current state) and the dynamic fields in the resulting state-embedding
+ vector are updated.
+ """
+ num_states = self.net_wrapper.num_pruned_layers()
+ network_obs = np.empty(shape=(num_states, ObservationLen))
+ for state_id, layer_id in enumerate(self.net_wrapper.model_metadata.pruned_idxs):
+ layer = self.net_wrapper.get_layer(layer_id)
+ layer_macs = self.net_wrapper.layer_macs(layer)
+ conv_module = distiller.model_find_module(self.model, layer.name)
+ obs = [state_id,
+ conv_module.out_channels,
+ conv_module.in_channels,
+ layer.ifm_h,
+ layer.ifm_w,
+ layer.stride[0],
+ layer.k,
+ distiller.volume(conv_module.weight),
+ layer_macs,
+ 0, 0, 0]
+ network_obs[state_id:] = np.array(obs)
+
+ # Feature normalization
+ for feature in range(ObservationLen):
+ feature_vec = network_obs[:, feature]
+ fmin = min(feature_vec)
+ fmax = max(feature_vec)
+ if fmax - fmin > 0:
+ network_obs[:, feature] = (feature_vec - fmin) / (fmax - fmin)
+ # msglogger.debug("model representation=\n{}".format(network_obs))
+ return network_obs
+
+ def rest_macs_raw(self):
+ """Return the number of remaining MACs in the layers following the current layer"""
+ rest, prunable_rest = 0, 0
+ prunable_layers, rest_layers, layers_to_ignore = list(), list(), list()
+
+ # Create a list of the IDs of the layers that are dependent on the current_layer.
+ # We want to ignore these layers when we compute prunable_layers (and prunable_rest).
+ for dependent_mod in self.current_layer().dependencies:
+ layers_to_ignore.append(self.net_wrapper.name2layer(dependent_mod).id)
+
+ for layer_id in range(self.current_layer_id+1, self.net_wrapper.model_metadata.num_layers()):
+ layer_macs = self.net_wrapper.layer_net_macs(self.net_wrapper.get_layer(layer_id))
+ if self.net_wrapper.model_metadata.is_reducible(layer_id):
+ if layer_id not in layers_to_ignore:
+ prunable_layers.append((layer_id, self.net_wrapper.get_layer(layer_id).name, layer_macs))
+ prunable_rest += layer_macs
+ else:
+ rest_layers.append((layer_id, self.net_wrapper.get_layer(layer_id).name, layer_macs))
+ rest += layer_macs
+
+ msglogger.debug("prunable_layers={} rest_layers={}".format(prunable_layers, rest_layers))
+ msglogger.debug("layer_id=%d, prunable_rest=%.3f rest=%.3f" % (self.current_layer_id, prunable_rest, rest))
+ return prunable_rest, rest
+
+ def rest_macs(self):
+ return sum(self.rest_macs_raw()) / self.original_model_macs
+
+ def is_macs_constraint_achieved(self, compressed_model_total_macs):
+ current_density = compressed_model_total_macs / self.original_model_macs
+ return self.amc_cfg.target_density >= current_density
+
+ def compute_reward(self, total_macs, total_nnz, log_stats=True):
+ """Compute the reward.
+
+ We use the validation dataset (the size of the validation dataset is
+ configured when the data-loader is instantiated)"""
+ distiller.log_weights_sparsity(self.model, -1, loggers=[self.pylogger])
+ compression = distiller.model_numel(self.model, param_dims=[4]) / self.original_model_size
+
+ # Fine-tune (this is a nop if self.amc_cfg.num_ft_epochs==0)
+ accuracies = self.net_wrapper.train(self.amc_cfg.num_ft_epochs, self.episode)
+ self.ft_stats_logger.add_record([self.episode, accuracies])
+
+ top1, top5, vloss = self.net_wrapper.validate()
+ reward = self.amc_cfg.reward_fn(self, top1, top5, vloss, total_macs)
+
+ if log_stats:
+ macs_normalized = total_macs/self.original_model_macs
+ msglogger.info("Total parameters left: %.2f%%" % (compression*100))
+ msglogger.info("Total compute left: %.2f%%" % (total_macs/self.original_model_macs*100))
+
+ stats = ('Performance/EpisodeEnd/',
+ OrderedDict([('Loss', vloss),
+ ('Top1', top1),
+ ('Top5', top5),
+ ('reward', reward),
+ ('total_macs', int(total_macs)),
+ ('macs_normalized', macs_normalized*100),
+ ('log(total_macs)', math.log(total_macs)),
+ ('total_nnz', int(total_nnz))]))
+ distiller.log_training_progress(stats, None, self.episode, steps_completed=0, total_steps=1,
+ log_freq=1, loggers=[self.tflogger, self.pylogger])
+ return reward, top1
+
+ def finalize_episode(self, top1, reward, total_macs, normalized_macs,
+ normalized_nnz, action_history, agent_action_history):
+ """Write the details of one network to the logger and create a checkpoint file"""
+ if reward > self.best_reward:
+ self.best_reward = reward
+ ckpt_name = self.save_checkpoint(is_best=True)
+ msglogger.info("Best reward={} episode={} top1={}".format(reward, self.episode, top1))
+ else:
+ ckpt_name = self.save_checkpoint(is_best=False)
+
+ import json
+ performance = self.net_wrapper.performance_summary()
+ fields = [self.episode, top1, reward, total_macs, normalized_macs, normalized_nnz,
+ ckpt_name, json.dumps(action_history), json.dumps(agent_action_history),
+ json.dumps(performance)]
+ self.stats_logger.add_record(fields)
+
+ def save_checkpoint(self, is_best=False):
+ """Save the learned-model checkpoint"""
+ episode = str(self.episode).zfill(3)
+ if is_best:
+ fname = "BEST_adc_episode_{}".format(episode)
+ else:
+ fname = "adc_episode_{}".format(episode)
+ if is_best or self.amc_cfg.save_chkpts:
+ # Always save the best episodes, and depending on amc_cfg.save_chkpts save all other episodes
+ scheduler = self.net_wrapper.create_scheduler()
+ self.services.save_checkpoint_fn(epoch=0, model=self.model,
+ scheduler=scheduler, name=fname)
+ del scheduler
+ return fname
+
+
+def get_network_details(model, dataset, dependency_type, layers_to_prune=None):
+ def make_conv(model, conv_module, g, name, seq_id, layer_id):
+ conv = SimpleNamespace()
+ conv.name = name
+ conv.id = layer_id
+ conv.t = seq_id
+ conv.k = conv_module.kernel_size[0]
+ conv.stride = conv_module.stride
+
+ # Use the SummaryGraph to obtain some other details of the models
+ conv_op = g.find_op(normalize_module_name(name))
+ assert conv_op is not None
+
+ conv.weights_vol = conv_op['attrs']['weights_vol']
+ conv.macs = conv_op['attrs']['MACs']
+ conv.n_ofm = conv_op['attrs']['n_ofm']
+ conv.n_ifm = conv_op['attrs']['n_ifm']
+ conv_pname = name + ".weight"
+ conv_p = distiller.model_find_param(model, conv_pname)
+ conv.ofm_h = g.param_shape(conv_op['outputs'][0])[2]
+ conv.ofm_w = g.param_shape(conv_op['outputs'][0])[3]
+ conv.ifm_h = g.param_shape(conv_op['inputs'][0])[2]
+ conv.ifm_w = g.param_shape(conv_op['inputs'][0])[3]
+ return conv
+
+ def make_fc(model, fc_module, g, name, seq_id, layer_id):
+ fc = SimpleNamespace()
+ fc.name = name
+ fc.id = layer_id
+ fc.t = seq_id
+
+ # Use the SummaryGraph to obtain some other details of the models
+ fc_op = g.find_op(normalize_module_name(name))
+ assert fc_op is not None
+
+ fc.weights_vol = fc_op['attrs']['weights_vol']
+ fc.macs = fc_op['attrs']['MACs']
+ fc.n_ofm = fc_op['attrs']['n_ofm']
+ fc.n_ifm = fc_op['attrs']['n_ifm']
+ fc_pname = name + ".weight"
+ fc_p = distiller.model_find_param(model, fc_pname)
+ return fc
+
+ dummy_input = distiller.get_dummy_input(dataset)
+ g = SummaryGraph(model, dummy_input)
+ all_layers = OrderedDict()
+ pruned_indices = list()
+ dependent_layers = set()
+ total_macs = 0
+ total_params = 0
+ layers = OrderedDict({mod_name: m for mod_name, m in model.named_modules()
+ if isinstance(m, (torch.nn.Conv2d, torch.nn.Linear))})
+ for layer_id, (name, m) in enumerate(layers.items()):
+ if isinstance(m, torch.nn.Conv2d):
+ conv = make_conv(model, m, g, name, seq_id=len(pruned_indices), layer_id=layer_id)
+ all_layers[layer_id] = conv
+ total_params += conv.weights_vol
+ total_macs += conv.macs
+
+ if layers_to_prune is None or name in layers_to_prune:
+ pruned_indices.append(layer_id)
+ # Find the data-dependent layers of this convolution
+ from utils.data_dependencies import find_dependencies
+ conv.dependencies = list()
+ find_dependencies(dependency_type, g, all_layers, name, conv.dependencies)
+ dependent_layers.add(tuple(conv.dependencies))
+ elif isinstance(m, torch.nn.Linear):
+ fc = make_fc(model, m, g, name, seq_id=len(pruned_indices), layer_id=layer_id)
+ all_layers[layer_id] = fc
+ total_macs += fc.macs
+ total_params += fc.weights_vol
+
+ def convert_layer_names_to_indices(layer_names):
+ """Args:
+ layer_names - list of layer names
+ Returns:
+ list of layer indices
+ """
+ layer_indices = [index for name in layer_names for index,
+ layer in all_layers.items() if layer.name == name[0]]
+ return layer_indices
+
+ dependent_indices = convert_layer_names_to_indices(dependent_layers)
+ return all_layers, pruned_indices, dependent_indices, total_macs, total_params
+
+
+import pandas as pd
+def sample_networks(net_wrapper, services):
+ """Sample networks from the posterior distribution.
+
+ 1. Sort the networks we discovered using AMC by their reward.
+ 2. Use the top 10% best-performing networks discovered by AMC to postulate a posterior distribution of the
+ density/sparsity of each layer:
+ p([layers-sparsity] | Top1, L1)
+ 3. Sample 100 networks from this distribution.
+ For each such network: fine-tune, score using Top1, and save
+ """
+ #fname = "logs/resnet20___2019.01.29-102912/amc.csv"
+ fname = "logs/resnet20___2019.02.03-210001/amc.csv"
+ df = pd.read_csv(fname)
+
+ #top1_sorted_df = df.sort_values(by=['top1'], ascending=False)
+ top1_sorted_df = df.sort_values(by=['reward'], ascending=False)
+ top10pct = top1_sorted_df[:int(len(df.index) * 0.1)]
+
+ original_macs, _ = net_wrapper.get_resources_requirements()
+ layer_sparsities_list = []
+ for index, row in top10pct.iterrows():
+ layer_sparsities = row['action_history']
+ layer_sparsities = layer_sparsities[1:-1].split(",") # convert from string to list
+ layer_sparsities = [float(sparsity) for sparsity in layer_sparsities]
+ layer_sparsities_list.append(layer_sparsities)
+
+ layer_sparsities = np.array(layer_sparsities_list)
+ mean = layer_sparsities.mean(axis=0)
+ cov = np.cov(layer_sparsities.T)
+ num_networks = 100
+ data = np.random.multivariate_normal(mean, cov, num_networks)
+
+ orig_model = net_wrapper.model
+ for i in range(num_networks):
+ model = copy.deepcopy(orig_model)
+ net_wrapper.reset(model)
+ for layer_id, sparsity_level in enumerate(data[i]):
+ sparsity_level = min(max(0, sparsity_level), ALMOST_ONE)
+ net_wrapper.remove_structures(layer_id,
+ fraction_to_prune=sparsity_level,
+ prune_what="channels")
+
+ net_wrapper.train(1)
+ top1, top5, vloss = net_wrapper.validate()
+
+ """Save the learned-model checkpoint"""
+ scheduler = net_wrapper.create_scheduler()
+ total_macs, _ = net_wrapper.get_resources_requirements()
+ fname = "{}_top1_{:2f}__density_{:2f}_sampled".format(net_wrapper.arch, top1, total_macs/original_macs)
+ services.save_checkpoint_fn(epoch=0, model=net_wrapper.model,
+ scheduler=scheduler, name=fname)
+ del scheduler
diff --git a/examples/auto_compression/amc/imgs/amc_worksheet.png b/examples/auto_compression/amc/imgs/amc_worksheet.png
new file mode 100644
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diff --git a/examples/auto_compression/amc/jupyter/amc_plain20.ipynb b/examples/auto_compression/amc/jupyter/amc_plain20.ipynb
new file mode 100644
index 0000000..3ccfb8c
--- /dev/null
+++ b/examples/auto_compression/amc/jupyter/amc_plain20.ipynb
@@ -0,0 +1,626 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Plain 20 - CIFAR10\n",
+ "\n",
+ "Plain20 is defined in Deep Residual Learning for Image Recognition and it is essentially ResNet20-CIFAR without the residual connections. You will need to train this network first and you can do that using [plain20_cifar_baseline_training.yaml](https://github.com/NervanaSystems/distiller/blob/new_amc/examples/auto_compression/plain20_cifar_baseline_training.yaml). The trained Plain20 achieves Top1 accuracy of 90.5% on CIFAR-10. The notebook uses data captured in Distiller AMC trace files.\n",
+ "\n",
+ "We search for a 50%-MACs-constrained (FLOPs-constrained) Plain20. From our Greedy Search algorithm we know that there exists a 50%-MACs-constrained Plain20 that can provide Top1=90%.\n",
+ "## Comparing multiple executions\n",
+ "\n",
+ "* How stable is the ranking of discovered networks?\n",
+ " \n",
+ "\n",
+ "Let's run 4 instances of AMC (Plain20, DDPG) and then review the results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd \n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib \n",
+ "import csv\n",
+ "from matplotlib.ticker import FuncFormatter\n",
+ "import ipywidgets as widgets\n",
+ "from ipywidgets import interactive, interact, Layout\n",
+ "import matplotlib.pylab as pylab\n",
+ "import matplotlib.animation as animation\n",
+ "from matplotlib import animation, rc\n",
+ "\n",
+ "from auto_compression_jupyter import *\n",
+ "\n",
+ "#plt.style.use('seaborn') # pretty matplotlib plots\n",
+ "\n",
+ "params = {'legend.fontsize': 'x-large',\n",
+ " 'figure.figsize': (15, 7),\n",
+ " 'axes.labelsize': 'x-large',\n",
+ " 'axes.titlesize':'xx-large',\n",
+ " 'xtick.labelsize':'x-large',\n",
+ " 'ytick.labelsize':'x-large'}\n",
+ "pylab.rcParams.update(params)\n",
+ "\n",
+ " \n",
+ "# Widen the cells to get entire rows in the screen.\n",
+ "from IPython.core.display import display, HTML\n",
+ "#display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
+ "\n",
+ "EXPERIMENTS_DIR = os.path.join(\"/experiments\", \"amc\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multiple experiment runs\n",
+ "\n",
+ "We run AMC for 800 episodes. We heat up for the first 100 episodes. During this phase we don't train: the Actor emits actions, to which we add a strong constant noise and fill the replay buffer (the AMC paper refers to this phase as the exploration phase). After completing the heatup phase (100 episodes) we start the learning phase (in AMC parlance this is the exploitation phase) in which we use an exponentially-decaying noise. After an episode ends, each transition in the trajectory is added to the replay-buffer using an uncommon scheme:\n",
+ "\n",
+ "> Following Block-QNN [54], which applies a variant form of Bellman's Equation [50], each transition in an episode is (st; at;R; st+1), where R is the reward after the network is compressed\n",
+ "\n",
+ "How should the learning process look like (e.g. the reward curve)? Unfortunately, the AMC paper provides very little information about this. The RL learning is stochastic and two executions of the same experiment can look quite different: one succeeding and another failing - so we need to run our experiments several time.\n",
+ "\n",
+ "Below we plot the execution of the same experiment several times.<br>\n",
+ "We configure AMC as follows:\n",
+ "- Architecture: Resnet20 (CIFAR10)\n",
+ "- Action range: 5% - 100% retain rate\n",
+ "- Target density: 50%\n",
+ "- Don't fine-tune a discovered network before providing a reward.\n",
+ "\n",
+ "The command-line is provided below:\n",
+ " \n",
+ " time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-ddpg-private amc.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=DDPG --amc-cfg=auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=private -j=1\n",
+ " \n",
+ "Each colored line represents one experiment execution instance. We plot the search-Top1 score of discovered networks as the RL-based AMC system learns to find better compressed networks. You might be impressed by:\n",
+ "* The variability in behavior, which is typical for RL algorithms.\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8c72593118c543dcb2be7bcf53e3f2ad",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), IntSlider(value=0, descr…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0: [62.28, 62.040000000000006, 62.0, 61.8, 61.56]\n",
+ "1: [65.12, 65.0, 64.68, 64.52, 64.4]\n",
+ "2: [65.0, 64.68, 63.88, 63.75999999999999, 63.75999999999999]\n",
+ "3: [63.8, 63.68000000000001, 63.6, 63.56, 63.56]\n"
+ ]
+ }
+ ],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR, \"plain20-ddpg-private\",\"2019.07.24-174636\")\n",
+ "df_list = load_experiment_instances(fpath)\n",
+ "\n",
+ "plot_experiment_comparison(df_list)\n",
+ "for i, df in enumerate(df_list):\n",
+ " print(\"%d: %s\" % (i, [score for score in df.sort_values(by=['top1'], ascending=False).top1[:5]]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Fine-tune for 2 epochs (using 16 processes): notice the low LR which we use because we only FT for 2 episodes.\n",
+ "\n",
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-ddpg-private/2019.07.24-174636 --arch=plain20_cifar --lr=0.001 --vs=0 -p=50 --compress=plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --epochs=2 --output-csv=ft_2epoch_results.csv --processes=16\n",
+ "\n",
+ "\n",
+ "Fine-tune for 60 epochs (using 16 processes): notice the x100 higher LR.\n",
+ "\n",
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-ddpg-private/2019.07.24-174636 --arch=plain20_cifar --lr=0.1 --vs=0 -p=50 --compress=plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --epochs=60 --output-csv=ft_60epoch_results.csv --processes=16"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Bubbles represent discovered networks\n",
+ "* Bubble sizes represent the relative number of FLOPs in each network\n",
+ "* Darker networks were discovered later in the search process "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best network: 83.14\n",
+ "Pearson: 0.902\n",
+ " exp search_top1 top1 name\n",
+ "59 2 60.92 83.14 BEST_adc_episode_221_checkpoint.pth.tar\n",
+ "58 2 60.84 82.92 BEST_adc_episode_211_checkpoint.pth.tar\n",
+ "71 2 64.68 82.82 BEST_adc_episode_588_checkpoint.pth.tar\n",
+ "11 1 58.20 82.72 BEST_adc_episode_266_checkpoint.pth.tar\n",
+ "68 2 63.56 82.72 BEST_adc_episode_424_checkpoint.pth.tar\n",
+ "Best network: 90.04\n",
+ "Pearson: 0.699\n",
+ " exp search_top1 top1 name\n",
+ "72 2 65.00 90.04 BEST_adc_episode_777_checkpoint.pth.tar\n",
+ "65 2 62.36 90.02 BEST_adc_episode_378_checkpoint.pth.tar\n",
+ "56 2 60.36 89.96 BEST_adc_episode_199_checkpoint.pth.tar\n",
+ "53 2 58.24 89.94 BEST_adc_episode_126_checkpoint.pth.tar\n",
+ "41 3 63.84 89.93 BEST_adc_episode_357_checkpoint.pth.tar\n",
+ "92\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_plain20_ft60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
+ "df_plain20_ft2 = pd.read_csv(os.path.join(fpath, \"ft_2epoch_results.csv\"))\n",
+ "\n",
+ "shorten_dir_name(df_plain20_ft2)\n",
+ "shorten_dir_name(df_plain20_ft60)\n",
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "plot_networks(df_plain20_ft2, edgecolors='green')\n",
+ "plot_networks(df_plain20_ft60, edgecolors='orange')\n",
+ "print(len(df_plain20_ft60.index))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's zoom-in on the FT60 networks.<br>\n",
+ "Remember, we ran our AMC experiment 4 times, and we fine-tuned networks discovered in each of these 4 experiments. Now let's separate these networks by color and observe how they cluster."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "plot_networks_by_experiment(df_plain20_ft60, edgecolors='red')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(0.32623976968406065, 0.026918228305542857)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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0KzMrMrPbzOwtM9tvZrVmttLM/mBmJ6Y6vu7GzE42s3+Y2VozqzOzrWY228x+bGb5qY6vPZnZX4Kb7RZf7RzHWDP7ZXDea4J9Tm7PfR4pM7uomfOzNtXxxTKz75jZJ1MdR1szs8daeb22a+JtZieY2a/NbG7w99uZ2QntuU8RaR+hVAcgIl2XmY0FngX6An8H7gVqgRHAR4HPmtkg59zG1EWZVBekcudm9kHgH8Aq4E/AJqAfcDxwE/A7oCvX3vweeDHm9xLgF8BTwGNJjONM4MvAUmAxcHIS932k7gZmxy2LvUa+A9yavHAS+g4wC3gwxXG0tV8BT8b8PgD4EfAI8K+Y5eF2juM84D/w1+pSQEmfSCelxE9E2kVQe/RPIA+Y6Jx7M279rcAtgKUgvKQys1znXLVzrj7FofwQn+yd7Jw7ELvCzHoAdckOyMzSgZBzrt337ZybA8yJ2Xc5PvFb5Jy7v733H+PvwAPOuQNm9gU6duI3u7lz45yLAJEkxtNtOOdeBV5t/N3MjsMnfm8n+Xr9K3C3c67KzG5GiZ9Ip6WmniLSXm4EhgI3xyd94G8YnXM/cs5taFxmZv2D5njbgmaIS8zs62Z2SHIY9JXbaGZDzexfZnYgeM/3zSs2s7+Z2e6geemfzSyniW1UmNm/zazSzHaY2W/MLC+u7Jlm9qCZrQmaOu00s8fNbGT8cQXNoO43s0vNbL6Z1QLfitnn9CbKn2Nm84LtrzOzLyfYdoaZ/cjMNptZtZm9ZmanJdpuE4YDb8YnfcHnsc85Vxu3v97B+dhgZvVBXHeYWXZcueuCc7g5KLfezO40s8K4clOD4/2cmd1sZqvwyebpMWU+ZGYzg8+t0nwT4cPOBZAbND/bHpyLZ81scCvOwRExs0HB57Mj+GwWmdlNCcrNNd+EeYyZvRjEvt3MfhV/7TnndiX6DFoZT18zazCznyVYlxZ8Bi/HLDvfzGaY2S7zzUrXmtl98TEdLUvQxy+47peZ2Qgze87MqoLvzK/MLCPBNiaY2TQz2xOc47fN7OpW7DvffLPcHsCldrDp46JgfZP96IJ47or5/big7HfM7Nog/jozW2pmH0jwfjOzL5nZO8F53WO+CXVFgrKT7GCz3o1m9j3a6cG7mQ0zs0eC46sN4vtcgnKLgvN8QnB9VJtv9v1/ZpYZW9Y5t8M5V9Ue8YpIcqnGT0TayxX4m/pWNb8ys2J8k7I+wG+A1cBlwM+BYfimiLFygBeA5/GJ1RXAf+GboX0M3yTpVmAKcB2wDd8kLFYuvunfjGAbk/BNmoYAl8SUuwoowzeP3AKU4xPbWWY21jm3PW67Jwfvvxu4B1jfwuEfDzwM/AH4cxD/r81siXPupZhyvweux9ek/hsYBTwD7AZa01x2DXCGmVU4595rrmDwecwFCoNjWBcc1zeB483sYudcY1+4m4D38LVn+4CT8OdnPP78x/sqkBFstwZ/TjFfm/AT4F3gjuC4xgIfBO6M28afgvXfw18z3wDuxzejbBNmVoavIewF3IU/Bx8C7jSzcufczXFvKcBfT//GN6k9C/gKMAh/fR4z59wW80n+x8zsFudcNGb12fhm1f8viP944GngHeD7QBUwGLgcXxNf04pd5ptZSdyy/a2ove6BPxfP4JsrNp6LzfjPliDGc4MYlwD/C1QDHwbuN7Nezrn4zz1WLXAN/nvxHtCYDO9txXE15SNA72CblcAXgcfMbFjsQyr8d/U6/N+3u/HNhm8C5pjZSc65dcHxnYA/D/uC46sBPov/DNqUmQ3Ef2dz8NfrZuBK4B4z6++cuz3uLcX4v59P4JuPnodvhdEXf15FpKtxzumll156tfkL2AW8cwTl/w9wwJUxywx/A+2AcTHLpwfLvhqzLAN/oxMFfha37XnA7rhljdu4I275z4LlF8csy00QbwU+sf1u3HIXvCYleM90YHqC8g3ASTHLsvCJ6qMxy8YFZR+Me/9ng+XT4/eXYP+fDMpG8AnNT/E3oImO77fAHmBw3PKbgm1c1ML5+XRQ7vSYZVODZduBorjy5fi+SjOArLh1FvPv24NtPBFX5mvB8jFHcM2VB+/5QRPrfx2svzRmWRo+UYkCFTHL5wZl/yduG78Jlp/dxD6+EKyffARxfybRNoE/4pOhouD3bwdxFrZ22zHbuijmWo5/fTym3E+Byrj3zg/K3RC3/EVgdczv6fiHEa8A6XFln8UnS3mtiHUv8K8Eyy8L4piQYN1O4K6Y348Lyu4BesddIxHgv2OWnROU/UzcNofiE9c/xh1HPTAsZlkPfJPrhLE1c5yNMX6nifV/CtZPiVkWCs5vGBgQs3xRUPaWuG38NVh+ShP7uDlYf8KRXlN66aVX6l9q6iki7aUQ2H8E5T8ArHTOPd64wDnn8DVAcPgT8ij+qXxj2TDwBj5Z/F1c2deAnmbWM8F+fxn3e2Otwfv7c85VN/47aF5WjK9tWg6ckmCb7zjn4gfEaM7rLqY5rPP93ebiazobXRr8/FXce/9KK2s4nHMPAufja6SOx9fe/RPYbmZfayxnZoavdXweqDKzksZXsAzg3JjtVgfvSzOzHkG5mcHqROfnQedcfMwfxt+k/o+L6+8XXAfx7o77fUbwc1h8wWPwAWCxc+7pmFii+GTH8IlFLMfhn89h11MbeBz/0OH9kSyD5nlXAk/HnNu9QZxXmNnR/n//Y/w1E/t6pRXvq8XXXseaAQwys8bWRqfhE6v78d/P2OvsafzfkGRPd/B359yOxl+cc2vxNb2x19Un8LWBT8fFvB94i+C7Yb5J9HnANOfcqpht7sMPdNXWLsf/LWn8LuB8H8yf479bl8SVr8c/4InVHteriHQQauopIu1lP77pW2uV45tuxlsS/BwSt3y7i+uTxsEEKL5pZePyXvgn+o0OOOe2xBZ0zm02swOx+zOzvvgb4MuB+ORxZ4KYVyVY1px1CZbtwTeVbFQe/FwRF2/EzNa0dkfOuReBF4O+VqPxI43eDPzCzHY75+7DN3XrhW/ielUTmypt/IeZnYofOGYykB1XrijBexOdnxHBz4WtPJT4c9b4ufZq5fubFSRKg/BJVrymrsmdzrndccvW4G+w48seNefcPjN7GrjSzL7kfLPLS/DnOrZp9d+Aa4G/4D/f6fjRIB+OfZjRgsXBNXOkNjrnGuKW7cHX8vXAtwho7CP7x+CVSCmAmZVy6LgEVe4o+0m2oKnvYux1NRLIB7Y2sY3GczsAf5+1PEGZRMuOmvnBtEo4dLTPRk1dr5vc4X333sM/wGiz61VEOg4lfiLSXpYAp5hZVnwNThuJv6lszbojHkE0SACeB/rj+7Atxj/tj+JrCxPVpLSm71SsNou3tYIa0neBd83sKfyN6KeB+2L2+yS+qWIijf3yyvE1QGvx/STX4o8/HXiOtjk/iST9nHUwD+JrSS/GT0fxCXzTyPdv/J1z1WZ2Jr7f48X42qd7gVvNbJJzbls7xtfc99Pifn4HWNBE2cYHAUvwfdIa/YbD+/3Ga24+xvQmlrfmujJ84vrxI9yGiEhKKfETkfbyJL4G6BP4GoeWrMEPVhJvdMz6tlZgZn1ja/3MrB++prJxf+PwfWuud879JfbNZtaLxDV+7WFt8HME8HpMDCH80/l3jnbDzrkVZrYHn9wC7MDX2Ga3orbng/hBci4NmsU1xnXYiKctaKzJHAe83FzBZHDORc1sHUd2TZYEA5LE1voNATITlD1WT+M/o0+a2Uv42uiHEjSTjeKbWM4AMLMr8P1mP48fGCeVVgY/K1txnX0Yfx4bxQ600lSC11gLfEgtfdDkO1FNdGutBM7AN6tsrtZxI75/YKLvwpF+P5rlnKs0s50cvDZjNXW99jezvLhavwp8Ytsef29FJMXUx09E2ss9+GTlJ8Hogocws5CZfdvMBgSLpgHDgxvTxjKGb4YIvi9ae/ha3O/fDH421pw0Pr2Pn1Li0/jR75LlmeDnV+OWf5pW3sSa2YVNLD8d35RtKbyfLDwCXGhmh43KaWbZZtbYjDfh+eHwEVRb8o9gW7eZWVbc/lJVizcNOM7MLo6L5Zv4ZGNaXHnj8M8n/npqE0Ez58fxCd81+JEcHzgkGN8XNd5bwc9E/V2TbTa+WfYtZnbYNRw07wTAOTfTOfdizCu2qWQliY9nFf5zOidu+dePMe6H8J/19xOtNLPeQcy1wEvA5WY2LGZ9D/ygTG1tGjAxqOVt3Fc6/ngjHPwb0igTP4pxrHa5XkWkY1CNn4i0C+cnp/4AflS7eWb2KH4kyVr8fHIfwY+C1zgR8R34AUUeMrPG6RwuxTdR+41zblE7hLkH+ETQh28ufj65TwHPO+cab5KW4ZtB/sz8PHFbgIn4Yf1Xt0NMCTnn3jWz+4BrzSwX3/x0ZBBv4w1uSx43s834G8Tl+JvXcfh+YNXAD2LKfhc/FcMLZvZX4E18clEBfBQ/kMh0fHPOWuAZM/s9vgns5RxhXzvn3FozuxXfl/INM3sE35xuTPA6/0i210Z+iD/Ox83P+bYeP+DL+fiRY1fEld8OfC4YVn8+vonlx4F/Oudi59Yrxk8TAAcHv7nWzKYG//5Fgr5XiTyIn97jDvyIttPj4zezifgb/rX4wVI+i0+wH2nF9tuVcy5sZtfjay+Xmtmf8DVNZfipQ86mdQnqPHxy9Z/47+Re59xzzrltZvYP4ObgYcJ7+GklTgSOun+gc+55M7sH+GrwUOsZfO1rOX7AnxkcbIb6n/jBpWYFf9dq8aOybgf6HW0MTbgN/917xszuxP+tuhL/Pf4f51z8lC8bgW+Z2XB8i4Fz8TWrDznn3mgsFPx9bExUzwh+3mBmjX0c7wiajotIB6fET0TajXNuoZmNw9eqfRCfLGXgm2m9hJ+6YVNQdpeZTcLPdXUt/iZ1Nf4J9C/aKcRqfL+nO/HTSdThRwS9JeYYImZ2aRDDV4L45+JrEeJHBG1vn8MPKPFp/KAsC/DD7t+Nv6FsyWfwN6aXBtvKxt8cPgn82DnXOAhE4+cxEX/jegX+MzmAvzG/E98/EOfcSjO7DP+5/RB/Tp/GjzgZP79hs5xzd5jZavycfP+Fr6VYSfuMgNiaeLYFtaE/wp+7giCer+DnSYt3AJ8Y3ol/iFEdlPt2XLneHF5bFDvJ9h/xc+615GX859cX+IM7dE4/8DWCZfjrpTd+kKMFwI3Oubmt2H67c869HAwOdCtwAz7R247vSxs/T2JTvokfbOW7wc/F+AcS4KfLiOLPr8M/MDmX4Po9hrg/b2Zz8E1mb8M/RNmIH832zzHl3jSz8/Ejwf4X/mHGvfgHKU8cSwwJYtoQXK8/DOLKxz/gudE594cEb9mFf3DwS3yt8QH8qJ63xpXrz+HX65di/v1L/HQRItLBWeJRskVEurZghMPhzrkBLZXtyIKmXDuAx5xzN6Y6nu7KzOYCJc654amORaQlZrYIiDjnTkh1LCKSPOrjJyLSSZhZToLFjbUkKR8QRURERDouNfUUEek8vmJm5+LnO6zE9zW8Ft9sLNF8cyIiIiKAEj8Rkc7kdfxgN9/GT4K9Dfg9cKsGVxAREZHmqI+fiIiIiIhIF6c+fiIiIiIiIl2cEj8REREREZEuTomfiIiIiIhIF6fET0REREREpItT4iciIiIiItLFKfETERERERHp4pT4iYiIiIiIdHFK/ERERERERLo4JX4iIiIiIiJdnBI/ERERERGRLk6Jn4iIiIiISBenxE9ERERERKSLU+InIiIiIiLSxSnxExERERER6eKU+ImIiIiIiHRxSvxERERERES6OCV+IiIiIiIiXZwSPxERERERkS5OiZ+IiIiIiEgXp8RPRERERESki1PiJyIiIiIi0sWFUh1AWyopKXHl5eXtuo+qqiry8vLadR8i7UHXrnRWunals9K1K52Vrt3ObcGCBTudc73jl3epxK+8vJz58+e36z6mT5/O1KlT23UfIu1B1650Vrp2pbPStSudla7dzs3M1iVarqaeIiIiIiIiXZwSPxERERERkS5OiZ+IiIiIiEgXp8RPRERERESki1PiJyIiIiIi0sUp8RMREREREenilPiJiIiIiIh0cUr8REREREREujglfiIiIiIiIl2cEj8REREREfSYZkQAACAASURBVJEuTomfiIiIiIhIF6fET0REREREpItT4iciIiIiItLFKfETERERERHp4pT4iYiIiIiIdHGhVAcgIiLSLvbtw82dS2TGDNyBA1iPHoSmTMEmToTCwlRHJyIiklRK/EREpOtZtYr6X/+aGgfhsjJcSW+spoaMadPIfeYZMr72NRg8ONVRioiIJI0SPxER6Vp276b+V7/iQHEJ0V693l/sMjKoLywksnMnhb/8JRm33w49eqQuThER6djCYdy777L3tRep2boRSw9ROPoE8iZNgfLyVEd3xJT4iYhIlxJ97TVq0tIOSfoOWV9SQvWe3RS+/jp2wQVJjk5ERDqFqir2/fEuFtetZ/Wo3lSeNpy0SAO91ixh7J/mMOz0y8i86FIwS3WkrZbUwV3MrMTM7jazzWZWZ2ZrzOxzMeuvMzOX4HVeMuMUEZHOq+GV6YT79mu2TLhPXyKvvJKkiEREpFNxjgMP/IlZOVt5+/wx7B9USjQzg0huNtvHDmbmJaNYPG8a0XlvpDrSI5K0xM/M8oGZwHDgE8BI4JPA0riiDUDfuNfMZMUpIiKdmHO4/ftxOTnNF8vNxe3bl6SgRESkU9m0iY0bF7PulOEJa/QasjN5d+Igdr00DZxLQYBHJ5lNPW8BcoHLnHN1wbK1iQo657YmKygREelCzLC8XKy2ttnkz2prsfz8JAYmIiKdRf3bC1henttsM86q0iK2RjbRe+NGGDgwidEdvWQ29bwSmAX8wsy2mNkyM/uJmeXGlUs3s9VBmelmdlkSYxQR6b6qq4m+NovKB/5M5QN/JvraLKiuTnVURyz9rLMIbdncbJmMLVtIP+usJEUkIiKdSXj/Xurym285ghmVuRlQVZWcoNqAuSRVT5pZDWDAY8AvgX7AXcCrzrmrgzKnA6OAd4As4KPA14EbnHP3NrHdG4EbAcrKyk5++OGH2/U4KisryddTYumEdO1Ks6qqCO/bTW0IIhnpAITCDWRHIKNHL8jLS1loR3ztRiK4zZtpyMyEtPTDVltDhLRIBOvXD9IPXy/SVvR3Vzqr7n7tun172RepJJyb1Wy5/L015BT3gczMJEXWOmefffYC59yE+OXJTPzqgF3AYOdcOFj2EeDvQLFzbncT7/srcLpzrqKlfUyYMMHNnz+/DaM+3PTp05k6dWq77kOkPXSpa9c5iEQgFOpUo2l1VNE3F7D0yXuYe94I6goPbYSRtb+a015cwegP3UjaSSenJL6juXbdokXU3303NZmZhPsPgKwsP4/fls3khMNk3HQTNmpU+wQsEuhSf3elW+n21+769Sy+93+Z8aFxTd5n5G7fyyXzKin97g8gLanjZbbIzBImfsns47cFWNuY9AUWBz8HAwkTP2A2fjAYEenuduwgOnMmDdOn42prITOT9Mlnkn72VOjTJ9XRdU4NDeyY9jBvTBlyWNIHUFeYyxtThlAy7WHKjj+h09SQ2XHHkfXf/03GzJk0zJyJq63FcnNJnzKFtDPPhN69Ux2iiIh0VAMHMrCsggFvrmbjycMOW51WH2bsG+soPufaDpf0NSeZid+rwNlmFnLORYJlI4Ofa5t530nAhvYMTEQ6gffeo/7Xd1KdESIyaDAuOxvq6gjNn0fuqzPJ/MIXsPHjUx1l57N8OeuzqqkpHtJkkZriQtZnbaBs+XIYMyaJwR2jsjLSPvpR0j7yEWho8EmraohFRKQlZhRe8znOvOdXvPvSYtaN6UtlWRFpDVF6rt7CmKV7qDjxAtJPn5TqSI9IMhO/nwJXAb81s5/jp2n4KXCfc24PgJndDrwBvIfv4/cR4AbgK0mMU0Q6mj17qL/zTg6UlREtKjq4PCuLSPkQDhw4QMHvfkfWbbdBWVnq4uyMdu1ia8+W/yvY1jMDdu1KQkDtwMw3CxYREWmtggJ63nQLZ765gLGzXqDu5XextHQKRh1PwSevhxEjOt3DxKT9T+ice8fMLgF+DLwNbMX377stplgh8BugD1ADLAOucs49nqw4RaTjic6eTU1a2qFJXwxXUEBNVhYZM2aQdtVVSY6uk0tPJ6Oh5WKhBtdpmnmKiIi0iaws0k6fRMnpkw7O19fJkr1YSW2U6px7yTl3inMu2zlX7py7xTlXHbP+G865Ic65HOdcL+fcJCV9ItLw6quE+/RttkykX38aZs1KUkRdyNChDN1SB9Fok0WsIcrQLfUw7PB+DiIiIt2CWadO+iDJiZ+IyNFwlZW4rOaHVHZZWbiammYTGEmgTx/69BtF2ZKmu1KXLt1An/6j1IxWRESkE1PiJyIdXlpREVZT02wZq6nB8vM71ehaHUXhx67lrDVG/wUrSa87OPByel2Y/gtWctYao/Cqa1IYoYiIiBwr9XYXkQ4v/eyzyXzqKeoKC5ssk7FlM+nnnpvEqLqQXr3o/eXvcMFz09jx1Bx29PD/NfTeF6H3+NPJ+fLl0ET/ShEREekclPiJSIdnp55KztPPENm+nYbS0sPWp+3eTU40StoZZ6Qgui6iqIicj1/DoMuuYNC2bX5ZWRnk56c2LhEREWkTSvxEpOMrKCDj61+j4Be/pOa95YTL+hDNycFqa8nYtpWctDQyv/Y16NUr1ZF2fvn5x5bsRSKweDGR116DvXuxnj1JP+MMP/+fplQQERFJGf0vLCKdw8CBZHzvfwjNm0dk+nTYuQMrKCD9ssuw006DZpqBSpLs2EHk17+mbvdu6noU4XJysA0byfr978kqLib0la9ASUmqo5SOprISli2Dmhr/0GH0aMjOTnVUIiJdjhI/6RhqaqC62v9nn5eX6miko8rPx84+m4yzz051JBKvqorwz3/OAQcNo0YfXF5URHXfvtRv3EjBL35B6NZbITc3dXFKx1FfT+20J9i+YAarS0NUZhs9qqMMeRxKz7iQzAsv0WBNHUFtLe6dd2iYPRtXVUVacTHpZ57pE3TN7SnSqSjxk9TasIHKl2awc9EyajKyyKyvo2ToIHqcexaMGpXq6ESkldwbb1BTWUlDxciE6yMDBlCzbBn58+ZhU6YkOTrpcCIRKv9yD6/XLGPlB0fSkJXx/qql1bWMffVpTtq3l5yPXd3p583q1NavJ3znndRWVxPuVYzLysLWbyDrN78lq38/QjfdpIGfRDoRPUqTlHELF7Lyznv5Z1UOT53zcZ4752NMO/9qnswawMI/PUr45VdSHaKItFLDSy8RLuvTbJn6Pn2IvPRS6zcaicDOnf5nQ8MxRigdiXvrLRbuXszys0YfkvQBRHKzeffcMaxY/hqsXJmiCIXduwn/4hfsz8mldtRoGkpLifboQUO/flSPGcOBvXuJ3HknhMMtb0tEOgQlfpIae/aw7v7HeHHChWyrGEc0IxMAl57O7sHDmT75Ayx/7lVYtSrFgYpIi5wjun070YKCZotFCwpwO3a0vL3qasLP/Zstt/2QJT+9m9ptO9hy2/8S+ffzvlm4dHp7Zr3AqrF9mqzNc6F0llX0oHL2jCRHJo2iM2ZQ7RzR3r0Tro8MLqd2yxZYtCjJkYnI0VJTT0mJ8Otv8G5xOTU9ixOuj+Tk8vaQ8QyeMYuCYcOSHJ10ePv3+z6hOTnQo0eqoxEzLDvbP/nPymq6WDiMNbMegMpKdt/1O+ZEC1h30sXUFRbRv3YNT55wPuXz3ua0dxbT80s3qi9wZxaJULV5DfunntBssX0De1P50jJaHGPWOVi/nuq5M6lavwrMKBw2lqzTJ0Of5muhuzTnYN06GmbOJLpqFaSlkX7ccaRNnuynamlONErDK68QHjCw2WL1xSVEXnmF0IkntmHgItJelPhJSuxZ8C5bhk9qtsyu8hHsef5+Choa1IFcvBUr2P/ys+xcv4Sa7BDZtQ2U9B9Bj7Mv9gMNSMqkTZxI6M03iZQPabJM+pbNpE08rdntHHj478zIKGPt+ImHLK/t0YtlE8+h/p05nPfYE+R9+lNtErekSGv67bWmTEMDtY89zKolr7JseCH7Ty6GaJRe6+cw5q4XKT/zg2RccFH36ycYiRB94AHqZs+htrCQhp49wTlCs2aR/cILZH34w9j55zd9XmpridbV4XJymt1NND8ft317OxyAiLQHJX6SEg11dUSyWvgPJZRBJC3d1yIo8ev2GubOYcXTf+Xt40vYM+E4XHoaRKMUrdvO+Id+wahzPk5o6jmpDrPbSp8yhZxXX6WypibhzaLV1JBTXU36lLOa3sjOnWxdtoZ15328ySJrx05g60uPMGzvXg0q0VmFQuSWDaJg8y4O9G96eo8eG3aQP2hEs5uqe3YaC9a9xsLLxhLNOPj/xKaSHmwbXc+EF55kfH4+oTPObLPwO4PoE09QPXcuNXEjb4YLCwnX1RF97DFyCgv9VDiJZGRg4PvWNvP/r4XDmnpDpBNRHz9JiaziXuTs3dVsmcyqA2RnpENmZpKikg5r61bW/us+Zl0wnN3D+/mkDyAtjb1D+jD7olGsfOlRWLs2pWF2awMGkPXJT5K/cgXpmzdDNOqXR6Okb95M/soVZH3qU9CvX9PbeO893iseiEtv+plkNJTByuL+8N57bXwAkky9zryAoYu2+OaICVikgZHL95B/RjNTt1RWsm328yw6q+KQpK9RJCeTtycPZdeL/+xegwPt30/4pZepGVGROGnLyqJ6cDmRJ588+D2Nl5FB+vHHk751a7O7Cm3fTuj009sgaBFJBiV+khI9zziVYWuXNFum/8pFlJxxiuZxEupmv8rCYbnUFySe/y2Sm83ikT2ofm16cgOTQ9hZZ5F9880U9imjcMli8pcuoXDJYgr79iX7lluwyZOb30A4TE2o5Qc9temZUF/fRlFLKthJJzG+xyhGzFpOWv2ho0KGauo57uUlVIw4HUY0U+O3aBEr+oRoyG76mqntVcCGrBpYsaKtQu/w3NtvU5sRgoyMpssUFhLevx9Wr26yTPp555Gze1eT3zXbv5+caBSbODHhehHpeNTUU1LCxo/nuOdfYcuyd9kyavxh64s2reWU3WvJnPyBFEQnHc2eha+z85y+zZbZOaIf+56YR677dPfrz9ORVFQQqqggdOCAH4AnNxdaGO3zfT160LtmPxtaKFZcvU/NPDu7UIiC67/A5Kceo+LJ11jZJ0RVdhqF1Q0M2+EoPf1Csi6+vNnvcmT3LvYVNp3cNNpTkA4HDrRl9B2a272baGYLgygBkVDID5TVlIoKsq68Eh5/nJrepTSUlvoHsZEIoS2byT1wgIwvfUnfRZFORImfpEZmJsVfvIELfv8nlr26jpUDR1Fb0IOM2moGb1jBmPAe+nzxM9CzZ6ojlQ4gWl9HQ2bzN3gNmSEaImHfdEyJX+oVFLQ+4Ws0ejTDHn6SRVUHqM9L/N6s/XsZWr8PRiaeKF46kawscq66msEXXc7gpUv9VB35+TBmjH9g0IL0nFwyayMtlsupi3arLgOWm4tFWp5bLy3a8nlJu/BCcgYOJOO552hYsphoeoj0aAPpEyaQfuGFMLD5UT9FpGNR4iep07MnvW7+KpOWLmXs3PnUbVhJRl4ORRdMwE44odlh4aV7ySouI2fXfir7JZ7+AyBn9wEyi4rVNLgzy8qi7MKpTHj+RV4/42Iasg4dNCJUW8Mp816i9JJzmm3GJp1MYSEcRXNBGzOGipcfZUND9GC/3zihmnoG73LNNxntYuy448h+/HHC0WjTfw/r6sgEGD685Q2OGUNozBhClZVQW+unUmlhtE8R6ZiU+ElqhUIwbhw9xo1LdSTSgfU8/RwGzX2IJc0kfgOWbaF4kpoGd3YZU6dwYk0NRS8/zuK+w9nVZxBpOWHK35vL2K2rKD9vEqEzW+grKN1DWRl9B41lwDtr2XDS0MPXO8ewN1ZRevKUVtUgdhn9+hEaNZqM9esIJ5pexTmyV68mdN65RzYiZ36+f4lIp6VH4yLS4aWdeBLj6npRtmhdwvUlyzdx/J5c0k/VIAOdnhmZl1zMyG99ictGFHHF9kX0jNRwaUUvKr79ZTIvvLB7N+Wtr8fNn0/tk49T989/wDvvQKTl5o5dVcHHP83kXYVUzFxK9u6gH59z5G3bw7gXF3Nq5jCyLvtgaoNMgdBnric/P5+sZcuwykq/0DnS9uwhZ8lickePIu3yy1MbpIgknWr8RKTjy86m+Mavcs69d7Fy42JWDy2iriCHjOpahqzcw4hoL4pv/KqeRnclpaVkfuBySoD06dPJnDo11RGlXPTNBWx94m+sLoqwtSwXnGPAv1+m/Iksyq76LDZmTKpDTL6CAnredAtTZ89i16wXOBBeh0UdPQp702vyJ0k7dWL3bBbcowehb32LglmziDz/PA3r1mEuSnpZGenXXONH4gzpFlCku9G3XkQ6h1696PmNWzll2TJGzX+N8La9ZOSXUHDxVX4wCN3EHLv6ety77xJd5Yd4Txs2FBs/vlsNjNFRRd9+i+VP3MOcs4dS2+vgwDfbx8Gy7XuZ/MCvGf7pb0BFRQqjTJGcHELnnk/Z2edSVlnpa4Tz87t3zTBAXh524YVknHceGVVVvr9fXp7Oi0g3pjslEek80tNh7FgKxo5NdSRdjnv7bSJ/+Qt14TDh3DwAMmbOICsjg9B11/kBlzqjaBQ2bfJTS+TlQf/+ne/GNxJhx5MPMHdK+SFJX6Pq0iLmTIpQ9OQDlNxye+c7vraSluYHipFDpafrvIgIoMRPRKTbcwsXUnf33VQOLsfFTMEQAWoPHCD/7rvJuukmrDMNwuQcbu5cGv71L8K799AQCpEeCZPRuzfpl1+OTZjQeRKkZctYm1NDTUmPJosc6FfM5jcXU7J2LQxJMKCHiIh0exrcRUSkO4tGiTzwAFUDBh6S9DVyBQVUDRhI5IEHfO1ZZ+Ac0aeeovrPf2Zfdg6VY8ZQU1FB5egx7AtlUPOHPxB99tlUR9lq0W1b2VLcQnNbM19m27bkBCUiIp2OEj8Rke7svfcI799PtKioySLRoiLC+/fDihVJDOwYrFxJ3bPPUj1q9KHHZUa0Z0+qRo6i/qmnYO3alIV4JCwt3U+23YL0qNM8liIi0iT9DyEi0p3t2EG4FaMehjMyYPv2JAR07BpeeYXaHkVNj+aYmUlNQSENM2YkN7CjZOXlDNlSD841XaYhyuCtdVBenrS4RESkc1HiJyLJ5VznaTLYHaSlQbTphOJ9rpPUJjlHw9tvE+nTp9likT59iL71VpKCOkbl5QzI70/P1VuaLNJ72QbKBoyG0tIkBiYiIp2JBncRkfYXjcLChex99QWq1q8A58jpX07PyRf40SI1FUPqDBlCVn0d9dFo04ldNEpWXX3nGTQkEvEjGTYnLQ3XWSY+N6Pnx6/nrHt+yuz6Deys6I9L959VWriB0mUbOGO1o/BLV6c4UBER6ch0tyUi7SsSofr+P7N485usGFPKvtPH4QwKN+1i6Ev3Mm7+KAqu/wJkZaU60u6pXz9CI0YQ2ryZyIABCYuENm8mNGI49OuX5OCOghnWrz9pe/cS7dWryWJpe/eS1r9/EgM7Rv370/8L3+LCaY+x7R+L2FqShTlHv1319B5+AgU3fRSKi1MdpYiIdGBK/LqraBRWr4bKSn/DPWyYJmmWdlH37DTm7XqbxReNPaRGaf/A3rw9oISqWcs544lHyP34tSmMsnsLXXst+XfcQeXatUQGDjxYW9bQQGjDBvLNl+ksQuefR+aDD1LbVOLnHFk7dpD+2c8kN7Bj1bcvhTd+mcKdOxmxZYufjqJ/f+jZM9WRiYi0Ledg82bYscP/nzR4sOajbANK/LqbYG6ryFNPEd6/n4ZQiLSGBjLSQ4QuOJ+0iy9uuYmUSGvV1LB97kssv2x44maEZqw6bTjDn5jDkIs/CD2anqdM2lFpKRnf/S6Fjz9O5K233h/sJSMcJnTiiaRfeSWUlKQ4yNazCRPIeeUVGtasJlw+5ND5+pwjc/VqcgYPwo4/PnVBHouSkk71eYiIHJFVq9g97RG27dnA9l6ZpDc4BuwMUzp2IrmXf1gJ4DFQ4tfNRJ97jtonnqB6cDmu/8FmXVZTQ/bTT5O7eTNpn/2skj9pG8uXs7bYiOQ03YwzmhFiRb8MhixeDJMmJTE4OURJCemf/zzpe/aQtSUYRKRv385Zm5SVReirXyX/nnsIL1lMbW4uLisbq6slp7qajNGjSb/hhqZH/RQRkdRYvpzVf/sVcyf0Zu+U495/cLeoPky/d99l8m9W0+umWyDBvLPSMiV+3cnmzdQ/+SRVI0cd1qzT5eRQM2o0LFhA3kknYRMmpChI6VJqaqjKafkhQlVOOtHqKg0z3BH07Nk5k714BQWEvvENQuvXkzV/Pm7/fqxnT9JOPhkGDDi0FlBERFIvHGbrg/fw6lkDqCo9dG7ZaGYGGycMZ/a8FZz7zFPkfOxTKQqyc1Pi1400zHyVmry8pvvypaVRV9aH7OefJ3TyyboxkmOXl0dhdUOLxQqrGkjL19M7aWNmMHgwaYMHpzoSERFpyeLFrM6vPSzpi7Vp3GB2/HMOgy67AvLykhhc16AH7N1IdOG7NPRufo6naEkJDWvXQX19kqKSLm3kSAbvTSOjsqbJIul1YYZvDcPYsUkMTERERDqSmpXL2NS/+YfADdmZbOmVDhs3JimqrkWJX3fS3DxdjczA0ATb0jaysug9+ULGvrYSixxe82cNUUa+9h59Jpyj9voiIiLdWDQaIZrecmuzaJrpPvUoKfHrRtKGDCFt9+5my9j+/aT17AnZ2UmKSrq6zPMv4qTBZ3DGM0soXr6BUE0doZp6eq3czMRnF3NqrxPIuvxDqQ5TREREUiin32B6b69utoxFGijdVQelzbdgk8TUx68bSZ86lewFb1LVr1+TNX9ZmzcR+shH1L9P2k5aGtkfu5rxK05l6GuvcOCZZUCUvEEjKPzwJ2HUqJZrokVERKRLSzvxJEY88zCrqmoJ5yWugCheuZnSIeOguDjJ0XUNSvy6kxEjyD75JBrefofaiopDp2yIRslYt5acsjLstNNSF6N0TWZQUUF+RQX5qY6lo9m5k/rX51C5eikA+eUVZE6cpKeZIiLSveTl0ffCj3DKiw8z/5wK6gtyDlndY912Ji2uouCLV6QowM5PiV93Ykba9deT++ijZL76KrUZmUSzMrFwmOzaWjLGjiX9+ushJ6flbYnIsXGO8Av/Zv2MJ1lansPu4f7pZc9NrzL6V88xaPJlZF50qWrfRUSk2widNZXj0tIo+fdjrCyGnb1zSIs0UL6phkFpxZR87mbo1y/VYXZaSvy6m4wM0q++mvRLLiHzrbdwe/dieXnYuHH6IokkUWTWTBbOfYL5l40mknNwipXKvr3YfFw9J784jROycwmdfU4KoxQREUkiM0JnTmHAKRPp/8471G/bRFp6BhmnV8CIEeoacoyU+HVXPXti55yD6hJEUiAcZscLT/LWucMPSfoaNWRn8vaUCvo9/xT9zpjc9NybIiIiXVF2NjZxIlmpjqOLUdosIpJsy5axriBCXY+mJ5+tL8hhTVEUlixJYmAiIiLSVanGr7NxDtavh3XrcA0NWJ8+ED9Qi4h0bPv2sbOg5eduuwrSYe/eJAQkIiIiXZ0Sv85k82b2PPxnNu3fwJq+Wbg0o9/cMIMi+fT+0Kd8Pz2ReJEI7N7tf4bDkJGR6ogkI4PssGuxWGbEqZmniIiItAklfp3Fli1s+v1PeHVcHruHjXt/pL+NwOLteznt0buocF/Cxo9PbZzScdTWEpkxnZ1zXmCP1VJbMZHN37uZkonnkDn1XMjXxAopM2IEQ/9Zz8pwA9GMxLX1FmlgxKY6uKoiycGJiIhIV6Q+fp3E/n/+ndmjstk9vN9hw7tXlRYxe0o52x7/q6/REamuZu/dP2f6sn/x9NQyXvngWA4U5fDMBYN4acPL7LzzDjUhTKVevSirmMDAt9c0WWTAO2spG34ClJQkMTCRFHDOt0rYtAn27Ut1NCIiXZZq/DqDHTvYtmEJO684rskiNSU9WJO3kT5LlsDxxycxOOmIqv/5GHNytrNm4uhDHhTUF+Sw5vSR1L6zhnMfvo/CL3wlhVF2b3kf+QST7v4FmTOXsn78QOqKfA1s1r4qBixcz6k1vcn/wtUpjlKkHTkHCxey+5Wn2bVjHbXZIXJrGigZOJIe517qh24XEZE2o8SvM9iyhS29s3DpzVfQbu6TS3jDOjKU+HVv+/ez4925rPvQyCYn/94ybjBbnlhE4dat0KdPkgMUAPLy6Pmlb3L2jOnsnP4ie91anBk9XRbFp11ExpSzITc31VGKtJvw88+xdM6TvHtSX/af5bswWEOUorVbOfG+nzHisutIn3haqsMUEekylPh1Fi2PA+GfnoqsXMna0hDRzGYGcUlLY0X/LCqWLfMjw0pq5OQQuuhi+px3Pn0am94WFUFIf5qli1u5kvdee4o5l4ymIfvgAEYuPY09w/oxq7SIzGn3MXTIUCgtTWGgIiJdh/r4dQb9+9N/Rx0WaWi22ICtNWQMHpKkoKTDCoepy2z5qx3OTCdaX5eEgKRFoZDvy1dSoqRPuoXKWS+zeEzPQ5K+WPUFuSwqz6Zu7qwkRyYi0nUp8esMiospHTKO0mUbmyySt20P5bW5MHp0EgOTDqmoiOJ9LQ/y02tfmPRexUkISEQkRjTK3iVvsntYv2aL7Rjeh30L5yUpKBGRrk+JXydR8MGrOGNlhN5LNkA0enCFcxRs2snkVzdS9rHPqrZAYPhwBkbyydm1v8kiGVW1DNsFHNf0gEEiIu2ioYEo0SanMnm/WFaGWiWIiLQhZQmdRe/e9PmPb3PBY/ez9YmFrOubTRTotytMv8wSSq79OlRovi8B0tMpvvDDnPz0n3j9gpGE87IPXV0X5oTp71E29cOQnd3ERkRE2kkoRHZ+Edl7Kqnt2fR8ork795NV0gn7INfV4d59l7pN63GWDgsXwpgxkN58oisi0t6UelqflQAAIABJREFU+HUmpaX0+I9v0GPrVkauW+dr/srKYMiQJkdvlO4p/dSJjKmtofCZR1k6MJOdA3uR3tCTQfNWMmptNYMmXUrGueenOkwR6Y7M6DXpPPotfZrVk0Y2Wax8+XaKpl6fxMCOXcPrc9k27UFW9nJsL80hlDOe15/+HUOeyKP04zfoAa2IpJQSv2TauZPIWwsI791Nem4+mceNh0GDjjxp69NHQ/BLi0JnTaV83PEMeON1DqxewpslaZzZdwqZH5oEvXunOjwR6cZCE0/npDkvse+9Teyq6H/oSufo984axtIHGz8+NQEehei8N1j69J94/bzh78/L2XtLFgsuGsvyLbuZfN8vGfqZm2Ho0BRHKiLdlRK/ZKivp/rxh9i8cDbLB+dQVZhN5rYww+c9zcDiYfS45nN+CHeRttazJ6ELL6InF5E+fTqZU6emOiIRESgooPTz3+S8e+9k5ZrFrBlaRDgvm6x9VQxfvZ9heYPo8bkvQmbiUT87nPp6tk17iDfOPpj0xars24u5E8IUPfkwvb7+XbXSEZGUUOLX3qJRqh74M6/vW8jyDx+HCx1s47/5REffhes46+6fUfyV70BeXgoDFRERSaLSUopvuY3ipUsZ89Zcwjv3k1U4kLyPnA4jRkBaJxp/bvFiVheGm+2zuHdwKVvfXkSvTZtgwIAkBici4inxa28rV7Ji49ssu3Ts4f+JmbFlfDkL9i3lnDmvETrvgtTEKCIikgqhEIwbR8G4camO5JhEtmxmW0lW84XM2FqcyZjt25X4iUhKKPFrZ5VzZvDeiKJmn1xuHDuAXa++8P/Zu+/4Ksv7/+Ov66zsQFghhBE2ArJRhghSQMT1Q1tnLVi1tX6t9au2Wju0tdragaN1V7/OllbFhYqIQhkqAkpU9p5hhJFB5jnn+v1xQkogOTkJZ+SE9/PxOA+S+7rOfd4H7oR8ct3XdZE5fkJ8/YZTaldejs3Nxb9uHViLo2tXzJAhGtEVEWmmjNOJ8fnr7efwW/0/LyIxo8Ivwo7s3krRyIygfcpapXGkYguUlqo4iHP2q6/wPvss5V4fFampYAyuZctJnDkT92WXYc4+W3M7RESaGWe37uQsKycvSB9HpY/O+yoCi7qJiMSACr8IczhdOOr7LaC1OH36LWC8s2vWUP7YYxR37oJNT68+7gMqSktJfuUVklwuzOjRsQspEm/89Y+iiMRc9+50drVh9ba9FHTJrLVL5urttOsxCFq1inI4EZEAVRoRlt57AK237g/eZ2c+ae27aDPteGYt3pkzOZLVoUbRV92clERJt+5U/vvfUFERg4AiccTrhS++gBn3w23Xw45t8PDv4csvweeLdTqREzkctL7qesYuP0TrdTtr3PbpqPTSYeVmztruJPX/XR7DkFLD/v2wahWsWQPFxbFOIxIVGvGLsIQRZ9H30Q/Zc1oZ3uQTCzvj89Pr6z1kTLpOtwDGs61b8e7bh/+0vnV2sSkpVFR68axaBYMHRzFcM2RtYCTI4dDXTXNTUQHPPQV5X0DP1jCwO5QkgM2HNx6F5cNh+g3gdsc6qUhNnTvT8cY7mfzOq+x74xv2tE6gsEMawxatoV2fYaTcfAlkBJ/6IVGwcycFs19j38617G2dgNMPHQ5U0Pb0ESRfcAmkpcU6oUjEqPCLtMxMciZexogPZrJyZBeKM1tW/6CaUHCE3ku30L/jsMDiHxK/8vOpdLnrLUIqPR7snj2oVGmk/HwqlnzCgU9XUFlajivBTeszh5Bw1ijIrP32Kokzb7wKh1bC2J41v546tYHsVvDZCninDVxyWewyitQlO5sWN95Ki3376Ll3Lwt27aLrnX+AFi1inUwAtm5l+9//wqcD0jk4vD/WGbjx7avySjp8tZJRf9tI65t/puJPmi0VflHgGjee/ukt6PDhW+RVrKIgzU1SmY/sUg+tR1+E+1sTNb8v3jmdIQ08Gb9fIxWNtWED2/7+Miva9SBvxMVUpKTiLj1C+81rGDLjcbpe8x1M//6xTikno6gIli+ACTm1/xLF4YChXeDjeXDu+VoMqzmxFiorA98fm8Mofrt2gceBAyr6mgq/n/x/PMPCM9tQ2KltjSZfgpsdw3vyybINfGv2LJKvnBajkCKRpcIvShxDhtJm8BDabN8e+OEmIQFyclQEQGA+z6ZNgXvsExOhR4/A30886doVT0UFZT4fOJ2197GWhIpyTI8e0c3WHBw+zI5nX2HugPEUt8uqPlyZlMKOfsM40CGHiS+9Srfbq37Ykvi0ejW0BjxB/mtKcENLP6xdC0OHRi2aRMju3fjmz8e3ZAm2shJHUhLOsWNxjB0LbdrEOp00J+vXs41DFHaq+xeEuwfksP+tz+iiWz6lmVLhF03GQJcusU7RdFiLd/FCDnw8mx0JpRSmukgq89HlsKHNyAl4zp0S2Nw3HmRk4Bo6FPfatVTmdK21i3NPHu5OnXQNNELl0s/5slWXGkXfsUoy2vBlZi+yl3xKwtSLo5xOwqa0FEL5nU+CgZKSiMeRyLJffEHFM89QkpyMt3sP8HgwZWW4Fy8macECPD/5SeAXgSJhULlpA9s6JAft40tws7u1my47dkDfuufsi8Qr3V8oMVPx3jus/M8/ePfstnw+uS9rz+rFlxNO493zcvhszQcceenZuFrBz3nZZaQmJuLZtLHmyp1eL66tW0krK8N1nRbxaYyDn3/J7pw+Qfvs6XYaB5d+EaVEEhEpKVBq6+9Xhm7zjHe7d1PxzDMUde6CN6creDwA2MREKrp1p6htOyr++lcoLIxxUGku/L5K/M76///1OY22kZFmS4WfxMaOHWxe+h4rJvalLCO1RlNlSiLfjD+Nb/asxH75ZYwCNkKLFrjuvJPU4cNpsWkjyevWkrx+Henr1pJ+Wh/cP/85tG8f65RxyXekhIrk4D/oVySn4C0tC8wVkvjUty8cdkB5Zd19Siug0AmnnRa9XBJ2vgULKElOxqam1truz8ig1Frs0qVRTibNlSerE+32lwXtY3x+2h2o0JQBabaiWvgZY9oYY54wxuw2xpQbY7YYY244rs+ZxphPjDFlxpg8Y8zvjTF1TJqSeFX66UJW90zHl1DHHEeHgw3923No8dzoBjtZ6ek4r74azx13kDJ+PCljx5Jw8804r7sO2rat//lSK1daKonFBUH7JBQV4E5L1YhqPEtJgVHnwrKt4KvlN+4+HyzbBmPOg6SkqMeTMLEW35IleDtkB+3mbZeJd/HiKIWS5s4MGECPg4aEwrpvE8/YnEdmh14q/KTZitoEKmNMKrAQ2AVcCWwDsgDnMX06AR8CrwM3AD2B5wAD3BWtrBJ5hRu+5tCY4N9YCzu2oXhRLq0qKqpvA2ry9u7F+49/4F27lgpPAjgM7nnzcHfqhOuqq6Br7fP/JLhWo4bT+bM1rGld95YN2ZtX02r0sCimkoi44GIoOATzl0DXNMhuHRjF3ZQHW4qhzxg474JYp5ST4fMFVvCs5/u6TUiAQ4eiFEqavYQEMs+/gjPmvMDn3+pJeXrN+X7pO/MZlVtAix/eGKOAIpEXzZUzfgokAxdYa8urjm09rs+PgELgOmutH1hljMkG/miMuc9aeyRqaSWirN+PrW9kxhisw8TPrXt791L54IMUuz14+/ar3qKj3Fqc+/aR8qc/k3D7bdC9e4yDxh/X8GEM/Hgxe3Zs5lCnbie0p+ftYOihbXhGaGGXuOdywTXfh3WjYeE8WLQe+nUD+sA146F3b43qxjunE5OcjCktxQYZuTUlJZhW2vBcwsc5chR9gdbvzmRjK8u+dkk4vH467y4hx9+SNtfdDp06xTqmSMREs/C7FFgMPGSMmQoUAO8A91hrj467jwbmVhV9R80B/gYMrnq+NAMpHbuRtns7B3p3rLvPvsMkp7eJm9E+7z//GSj6Oh73nozBl5lJsdOJ87nncN13n/ZtbKjUVLJuvJZzn/w/vtqzle05fSlPTcNTUkzHLWsZWLSb7Bu+Bxn6IbFZcDgCc/iOzuNbsADGjYtlIgknY3COH4/744+p6F73qp2e/ftwXnFFFIPJqcA5chQdhgwlKzeXij07cThcuM/oEfilUl3bMYk0E8ZGaTTFGFNK4JbN14CHgQ4ECrpF1tqrq/qsB16z1t59zPNSgGLgMmvtq7Wc9wfADwAyMzOHzpw5M6Lvo7i4mNQ6JqNLA5SXU3pwL8UtkgJXRS2SispITc6AePj79nqxu3fjS0ykzjcEOMpKcWRmxmSfwmZx7fr9UFKCt7gE6/dhHA5cKcmBuWEqpputZnHtSk0+H3b3bvwuF9Z54u+gTWUlDiymQ4e4HuHVtSvxStdufDvnnHNWWGtPmP8SzRE/B3AAuNZaWwlgjPEArxpjfmytPdiYk1prnwaeBhg2bJgdF+HfCi9YsIBIv8Ypwe+n5MVn+XztUtaO6Y3f7arR1nnFZs4qak3Lm74T2NS9qVuxgsK336a8Z6+g3dwbN9Li/CmYGFxDunYlXunabaa2bKHy0Ucp9XqpbJeJTUjAlJTgyd9PYkoq7v+9Ne5XQta1K/FK127zFM3CLw/YerToq7Kq6s8uwMGqPsd/lz+6mkNeZONJVDkcJF89nTPffI0ubyxkXUcPR1I9JJRW0mtnOVldTifth9Pio+iD0Ochmgb0FRFpzrp2xf3b3+Jatiyweuf+fZiMDJxXXYUZPFgrt4qIhFk0C79FwDnGGJe11lt1rHfVn1ur/lwCXGOMcRwzz28yUALE0YZuEhK3m6TvXEnXSVPokrsSb+FhnEkpOP/f6ZBZ9+qNTVJWFp7ycsqtDXpbkruiEpMdfAlzEZFTRloaZvx43OPHxzqJiEizF83C78/AZcDjxpgZBLZy+DPworX26HrNTwA3A89U9ekO3Af8VSt6NmMtWuA4eyzxsYRLHTp0wNWtG849e/BlZdXaxVFQgCclWRtPi4iIiEjUhbQagjHmHWPMBcY0foa1tTYXmEJgdc6VwP8BbxDYwuFonx3AJOA0YAWBuXtPA79o7OuKRIUxuK68ktTCApz79p1wO6fj8GFSdmzHNW2aVg0TERERkagLdcSvCHgVyDfGPAc8a63d3tAXs9Z+BAyvp89nwKiGnlsk5jp3xnPHHaQ/+xwVa1ZT4Q6MYbq9lXjS0nD9+MeY/v1jHFJERERETkUhFX7W2quMMRnA94DrgF8YYz4EngLesdb6IphRJH7k5OD67W9wbdpE0vbt4PcHliPX/kAiIiIiEkMhz/Grmof3CPCIMeZM4Abgn8ChqlHAx621uyMTUySOGAM9emB61L0xsYiIiIhINDV4x+OqDdVPBwYAbgLz9S4ENhtjrg1vPBERERERETlZIRd+xpjhxpinCeyn9xvgA6CbtXaKtXYg8BPgwcjEFBERERERkcYKdVXPXOBTAhutTwc6W2t/ddwCL68DbcKeUERERERERE5KqHP83gOmWms319XBWptvjEkKTywREREREREJl1BX9fx5iP3KTy6OiIiIiIiIhFuot3q+bIy5s5bjPzPGvBj+WCIiIiIiIhIuoS7uMgH4sJbjc6vaRKSpsRY2bMD3zDNU3nordscOvH/9K6xeDX5/rNOJiIiISBSFOsevJVBQy/FioFX44ohIWPj9+P45k4qF/6GsRUu8HTvh83go3J1HwiOPkDhkCM7vfx/c7lgnFREREZEoCHXEbwswvpbj44Ft4YsjIuHg//BDShctpPi0vng7dYKEBDAOfB06UNK3HyW5X+F77fVYxxQRERGRKAm18HsK+JMx5kfGmB5Vj5uAP1a1iUhTUVGB9733KOvWHZzOE9sdDsp69MC78D9QWBj9fCIiIiISdaGu6vmwMSYLePiY53iBR621MyIVTkQaYf16Kr1ebFKQ3VXcbspdLhJWrYKRI6OXLRz8fti5E44cgaQk6NwZHKH+DktERETk1BTqHD+stXcaY34PnF516Gtr7eHIxBKRRjtyBG9tI33H8Tud2IICTBQihYW12KVL8b3zDpUHD+JzuXD4fLjT03FNmYIZMwZM3LwbERERkagKufADqCr0FkUoi4iEQ1ISTl/9q3Yanw+TmhqFQOHhf/ddyt5+m9JOnfH37Vd93BQWkvTKKyTt3Yvj299W8SciIiJSi5ALP2PMCOBKoAvgObbNWjslzLlEpLF69cJjoLS8PLCoS218PhIrK6Ffv9rbm5pt26h45x2O9O4DnhrffrDp6ZT0OQ0zbx7Jp58OffrEKKSIiIhI0xXqBu6XERjp6w2cBxigFzAKyI9YOhFpuMREXBMmkLhpY+379VmLZ9NG3CNGQEZG9PM1gm/BAkrT0k8o+qq5XJRltML38cfRDSYiIqHbuxe+/hq++QYKatslTEQiKdQRv7uBn1Yt8lIE/A+BbRyeA9ZFKpyINI7jggtIyc/HsWwZ5W3b4mvbDrA48vNJ2LuXxF49cVxxRaxjhsy/ciW+DtlB+/jat8eXm4vTWt3uKSISLn4/rF8PXy6DkiLIaAvDzoROnUL/XrtjBwXvvMrevPXsaZ2A02/Jzq+kXd8zSL5gKrRsGdn3ICJA6IVfT+Ctqo8rgRRrrc8Y80fgA+CBSIQTkUZyuXB8//uknHEGCR9+iH/VNzhbtqCFx43zuu9jBg8GV4Om+MaW14utb+VOhwP8FlT4iYiEx/798Pe/QukuyE6CRDfs+ho+fx9yBsP3roPk5ODn2LKF7c/N4JMB6Rwc0b96FeavK71kff0Nox/bQJv/+ZmKP5EoCPUnv0Lg6Fd2HtAD+BpIAFpEIJeInCyHAwYMwDVgAPj9mIULcU2bFutUjWI6d8Z5+DC+du3q7OM4fBjTIUtbO4iIhENRETz+J8gug+49arb1s/DFV/DcE/CjW2vfMxbA7yf/lWdYeGZbCju2qdnkdrFrSHcWf7mJiW+9Rsq06yP0RkTkqFB/QvoMOKvq4/eAvxhj7gH+r6pNRJqyOC+GXBMm4MnfHxjNq4Nn7x5ckyZFMZWISDO2ZBEkH4TuWSe2GQNDusK+VbB2bd3nWLeOrY7DJxR9x9rTvwv71q3QnD+RKAj1p8GfAp9XfXwvMB+4GtgO3BD+WCIixzj9dBK7d8ezsZYFa6zFvWUzSVlZmGHDYpNPRKQ58fth8QfQs5ai7yhjICcdFn9UZ5fKTRvY3iH4raB+t4udbd2wbVtj04pIiOq91dMY4wSyga8ArLVHgOsinEvixf79sH17YCQmMxM6dtT8Kgk/lwvXTTeR+vzzVOZ+RVlyEv6EBBzl5SSWluLq3RvX9dfXvX2FiIiErqwMyo9Ai/bB+7VJh5U762z2+734nfX/TOB3OmpfhVpEwqrewq9qEZe5QB/gUOQjSVzIz4fXXoHNX0Hrqnv7C3zQKgcuuRq6dYtpPGmGkpNx3nQTzt278axYgT10CNOyJY7Bg/ULBxGRcHK5Aotl+f3BpwpUesHprrPZ074j7TaVsS/Ya/n9tDtQDkHmcItIeIS6uMsaAqN+WyKYReJFfj48+gB0KIdJ3cBZ9Z+CtbDzADz1B7jhZ9CjR/DziDRGhw44OnSIdQoRkebL44Gc02DnLugcpCDbcQAGTKmz2QwcSI+3X2ZDYQnl6bXf8pmxdR/tW+eAvq+LRFyoc/x+BvzJGDPEGP1a/ZQ3ayZkVUDvjv8t+iAw4tKpDQxqCS8/BT5f7DKKiIhI4407F9YfBm8d/5cXl0KeH0aMqvscCQlkTrmc4R9twFNUckJz6u4DjPziABkXXR6m0CISTKgjfq8DicAywGeMKTu20VqbHu5g0kTl58OGL+HcrnX3aZ8BazfBunXQt2/0somIiEh49O8PgybBfz6AAe2hbdXuXX4/7D4IXx+EqTdA27ZBT+McOYq+1k+r9//NxtaQ3y4Z/H467Sqha0UK7ab/L+TkRP79iEjIhd8dEU0h8WPnzsCcvrr27DmqjQu2blbhJyIiEo+MgW9fAdmd4aPZkLsJPA4o8UH7HnDt9XDaaSGdxzV6DB2HDic7N5eK3TvA6SBhcK/A8+v7eUJEwiakws9a+1Skg0icsBYI4W5fY4LuuSYiIiJNnDEwajSMGAl5eVBeDmlp9Y7y1SoxEXPmmSRwZvhzikhIQir8jDFBl1qy1gZdsEmakfbt4bC3/pW+DlXC6I7RyyUiIiKR4XBAdnasU4jISQr1Vs89QLDhG43TnyqysiCrF2zbDV3r2N/nUDGUJQfmB4iIiIiISMyFWvhNPO5zNzAUuAG4O6yJpOm75Cp47AHwHIDs1jXbDhXD0t1w2c3grntvHxERERERiZ5Q5/h9VMvhOcaYzcAVwD/Cmkqatk6d4MY74ZVnYO1GaOMEh4HDPqhIhct/DEOGxDqliIiIiIhUCXXEry5LgWfCEUTiTE4O/Pw+2LQJtmwOzPnrkB1YxdN1spdVjFRUYL/6Ct+iRdgDBzFpqThHj8YMGQLJtW88Kw3k9cLBg4GFfzIyApsEi4iIiEjEnexP6N8FtLDLqcrhgJ49A494l5+P95FHKDtwkIrWrfGnpGBKS/HMnEnirDdw/+QW6NIl1injV2kp/vnz8X34Id7ycgCcbjeuc87BMWECpKbGOKCIiIhI8xbqqp5fU3NxFwO0BzKAmyKQSyR6ysrwPvQQRV4f3mP2JLKpqZS1bkPF/v2kPfQQnl//Glq1imHQOHXkCN6HHqIkL4+KTp2xKSkAmNJS3B99TMqKFbjuuANatIhxUBEREZHmK8h6/DW8e9zjbeB3wGBr7dMRyiYSFXblSkoPH8bbsfbtJ/xt21IK+Bcvjm6wZsL/r39xZP9+yvucVl30AdikJCp69aKopATvCy/EMKGIiIhI8xfq4i53RTqIxKFdu/AtXIT/m6/B78fRvTvOsWOhR4/Apq9xwvfxx1S0DbpVJZUdsvF99BGOCy+Mq/cWcwUFVH7+ORW9+9TZxdu5C95Vq3Dt3QuZmVEMJyIiInLqCPVWz0GA01q74rjjQwGvtTY3EuGkibIW/7vvUvHObEpTkvG1bQcOB871G0hYtpzEM4bjmDYtbhZ5sfkH8HfoELxPUhL+0lKoqICEhCglawY2bKDC5Q5+LTgclHs8JK5fr8JPREREJEJCvdXzcaBXLcd7AI+FL47EA7tkCaVvv01Rr154u3bDpqZik5PxdurEkb59ObJ8Bb7XXo91zNAlJ2GqFhypk9eLcTjipphtMior8TvqHyG1xoEtK4tCIBEREZFTU6iFX39gWS3HlwOnhy+ONHk+H96336Y0p2vtS/E7HJT16oV3wXwoKIh+vkZwjRyJe1/wxWldebtxnXEGOJ1RStVMtGyJy+utt5vL58Vo4RwRERGRiAm18ANIq+VYiwaeQ05WWRn2s8/wPfMM3ieewM6dG90Ca/NmKouLsWm1XQ5VXC7K3G5sbnzcAWxGjSLJ58UUFtbeXlpKUkEhjvHjo5ysGejZE09yMqaoqM4uprQUj8MB/fpFMZiIiIjIqSXUou0z4JZajv+EwCbuEg3r1lF5110Uv/wyBVu2UrhrF4WzZ1N+113Y+fOjk6GoCG8Itzv63R7soUNRCBQGGRm4b7yRtF07cW3bBpWVgeM+H86dO0ndtJGEq68KbFovDeNy4br0UpK3boHabqetqCBp40bcF10EiYnRzyciIiJyigh1wtKvgPnGmL7Ah1XHJgIDgHMiEUyOs3Mn5Y88QnFWB/wtW1Yf9mW2p7KsjOR//JOklBTMGWdENkdiIk6/v95uDp8Xc8zS/U2d6d+fhF/8AtdHH+H79DP8WIzfj2vQIJyTboDu3WMdMW6ZESNIKi3F+e9/U5aQiDejJWBwHT5EQlkZnosuxEyYEOuYIiIiIs1aqNs5LDXGjAbuBq6sOrwC+JG19otIhZP/8s2ZQ0lKao2i7yibmEhply64Z83CPWwYOCJ492337rgdDkxZGbauERq/n4SyMszpcTb9Mzsb5/e+h/OKK6C0NLB6p0ahTp4xmPHjSRg8GM+nn+JbswasxTF4EI5Ro6Bt21gnFBEREWn2Ql6i0Fr7JfCdCGaRupSU4F2+HG+QvdD8LVpQuXsX7i1bIjs6lZCAa+JEEt57j7I+p9VaZHq2bsE9YED8Ls3v8dS+cI2cnIwMzJQpuKZMiXUSERERkVNOqPv4TQCw1s477vi3qo5/FP5oUu3IEfwOZ71bCfhcLgiyiEa4OM47j+TduzFffEF5Znv8bdqAMTgKCvDs3kVSh2yc06ZFPIeIiIiIiIQm1BG/PwK/reV4MnAvMDRcgaQWSUk4/D7w+4Pexun0+SA5OfJ5XC6c119Pypdfkjh3Lr5vvg4Ufq1b47rsMsyIEbpFUkRERESkCQm18OsN1LY2/zfUvrG7hFNqKs6+/XDm5eHr0KHWLubIEdwJidCtW3QyOZ2YYcNwDR2Kq7IyUJQmJICpf7NuERERERGJrlBXASkHaluBIRPwhS+O1MV1/hSSDxzAHDlyYmNlJUlbNuO68IJ6bwcNO2MC8+ESE1X0iYiIiIg0UaEWfv8Bfm2McR89YIzxENjmYUEEcsnxuncn4YbrSdu+Dc/GjTgOHcJRUIBry2bS1q0jafJkzLhxsU4pIiIiIiJNUKjDQ3cCS4CNxphFVcfGACnAWZEIJicyw4eT0L077k8/xZ+bi/X7cQw/A+eYs6Bjx1jHExERERGRJirUffzWG2MGAbcCQ6oO/wt41Fq7M1LhpBatWuE4/3wc558f6yQiIiIiIhInGrKP3y7gp8cfN8Z0VPEnIiIiIiLSdIU6x68GE3ChMeZdYEuYM4mIiIiIiEgYNajwM8Z0NMb8BtgOzATKgKsjEUxERERERETCo95bPY0xBrgAuBGYCHwCZAHDrLUrIxtPRERERERETlbQEb9jRvceApYCvay14wALVEQ8nYiIiIiIiJy0+kb8fgHcD9xrrbVRyCMiIiIiIiJhVt8cv/uA6cAWY8xvjTHdIh9JRERHZd/0AAAgAElEQVREREREwilo4Wet/Q3QFbgZGASsrdrA3QCJkY8nIiIiIiIiJ6vexV2stX5gNjDbGNMRuA7oDHxijPkQ+Je19uXIxmwmKipg7VooKoLEROjVC9LSYp1KRERERESauZA3cAeo2qj9N8aY+4ApwA+B5wAVfsFYi503j8rZs6nwevE5nTj8fjxeL64xY3B++9uQkBDrlCIiIiIi0kw1qPA76rhRwOzwRmpmrMU/axalc+dS2q07Njm5uqmsspKEzz4jee9eXD/+MbjdMQwqIiIiIiLNVYM2cK+NtXZXOII0Wzt2UDF3LiW9+9Qo+gBwuynv2YuyDRuwn38em3wiIiIiItLsnXThJ8H5Fi2iNDW17tE8YyjP6oDvgw9AO2aIiIiIiEgEqPCLMP/qNfjatA3ep2VLfHv3QmlplFKJiIiIiMipRIVfxFkwJniXo+0a8RMRERERkQhQ4Rdhjl69cObnB+9TUICjdWs4fg6giIiIiIhIGJxU4WeMyTbGPB6uMM2Rc+xYEouKwOervYO1eHbtwnXuufWPDIqIiIiIiDTCyY74tSGwl5/UpUsXPGefTdLaNVBeXrPN58OzaRPJOV0wI0bEJp+IiIiIiDR7QffxM8ZcUs/zu4YxS/NkDM4rryA5PQ3PnDmUG4PP4cRYP4kVFbiGD8d51VXawF1ERERERCKmvg3cXwMsEOwexJBWJDHG3AvcU0tTT2vtRmPMdOD/ammfaK2dF8prNFkOB44LL8TzrW/hWbUKW1iISUqCPn2gVatYpxMRERERkWauvsJvH/A/wOw62gcCnzbg9bYCI487tv+Yj31Ax+PaDzbg/E1bcjIMHx60ihYREREREQm3+gq/L4G+1trXa2s0xpQTfDTweD5r7Z5gHeprFxERERERkYapb3GX54CdQdp3AD9qwOt1NMbsrHq8b4wZdVy70xiz2RiTZ4xZYIy5oAHnFhERERERkVoYG6VNw40xU4AWwGogncBqoFcCk621HxpjRgJ9gFwgAfgO8L/A9dbaZ4Oc9wfADwAyMzOHzpw5M6Lvo7i4mNTU1Ii+hkgk6No9jrVQWRn42OUCh7Y1bap07Uq80rUr8UrXbnw755xzVlhrhx1/vMGFnzHGXfW8ipMNZYxZAFRYayfV0f4CMNJa2yuU8w0bNswuX778ZGMFtWDBAsaNGxfR1xCJBF27VQoL8b37Hr7Fi/BW7Z3p8vtxjh6N8/zzoWXLGAeU4+nalXila1fila7d+GaMqbXwC/lX3MaYG4wx64ASoMQYs75qtO1kfArkBGn/pJ52EZHQFRTgffBBij/7lIJu3TnSuw9HevehoHsPipYvp/L3v4cDB2KdUkRERCTsQir8qrZieITA6p6XA5cB7wAPVbU11hAC8wQb2y4iEjLfP/5BUWkpFT16gsfz3waPh8pu3Sm2Fu8LL8QuoIiIiEiE1Leq51E3AT+y1h77E9EsY8xXwJ+Ae+s7gTFmBoHCcSuBOX43ABOBi6va7wU+B9YTmOP3beB64JYQM4qI1C0/n8rcXLx9Tquzize7I97Vq3Dl5UFWVhTDiYiIiERWqIVfIrCkluNLgKQQz5EFvAi0BQqAr4AJ1tqPq9rTgceA9kApsBa4rK6tJEREGmTTJircbnA66+7jcFDu8ZC4caMKPxEREWlWQi38Xidwi+f9xx3/DjArlBNYa6+sp/024LYQ84iINIzPhzX1bztqHQ5sZWWDNigVERERaepCLfy2AD81xowBPqs6NgI4E5hhjKku2Ky1M8IbUUQkDFq1wu3zUd9yxC6vF9OmTVQiiYiIiERLqIXfjwjcfjmw6nFUGYH5f0dZQIWfiDQ9PXviTknBUVCAv0WLWruYI0dI8HjgtLrnAYqIiIjEo5AKP2utJruISHxzOnFdcQXJTzzJkR49sMnJNZpNaSnJmzfhmj4d3O7YZBQRERGJkFBH/KoZY5wA1lpf+OOIiESOGTKExO9fi/PllykzDrzp6QC4iotIrKzEfdVVmJEjY5xSREREJPxCLvyMMVcCvwB6Vn2+HrjfWjszQtlERMLOjByJZ8AA3MuX41+/HgBHjx6Y4cMhNTXG6UREREQiI6TCzxhzE/AQ8CxwX9XhccDzxpiW1tonIxNPRCQCUlIwY8fiHDs21klEREREoiLUEb//BW6z1j52zLF/GWNWEdiCQYWfiIiIiIhIE+UIsV9nYE4tx+dUtYmIiIiIiEgTFWrhtxsYXcvxs6raREREREREpIkK9VbPp4HHjDFdgUVVx84Gbgfuj0QwERERERERCY9Q9/H7vTGmArgTuKfqcD7wa2vtQ5EKJyIiIiIiIicvaOFnjOkM7LABfwH+YoxpC2Ct3R+NgCIiIiIiInJy6pvjtwVoe+wBa+1+FX0iIiIiIiLxo77Cz0QlhYiIiIiIiERMqKt6ioiIiIiISJwKZXGXS4wxhcE6WGv/EaY8zVt5OaxZA0VFkJgIvXtDenqsU4mIiIiISDMXSuH3eD3tFlDhF4zfDx/NhY/fgZQKSAIqgX9bGHI2TL0sUAiKiIiIiIhEQCiFX3tr7b6IJ2murIU3XoOV78HoLpByTIFX4YXcRfD0PrjxFvB4YpdTRERERESaLc3xi7QdO2DZHBjTvWbRB+BxwbCucHgVfP55bPKJiIiIiEizp8Iv0pYsgM5J4K5jcNUY6JMJC98PjA6KiIiIiIiEWX2F33+AimgEabY2r4EOGcH7tEmHg3ugtDQ6mURERERE5JQSdI6ftfacaAURERERERGRyNCtnpHW9TTYfTB4n/xCaJkJSUnRySQiIiIiIqcUFX6RNnosbC+DSm/t7dbC2r0w9rzAfD8REREREZEwU+EXaZ07w7BzYfFmKC2v2VbpheVboMVpcOaZscknIiIiIiLNXij7+MnJMAYu+Q6kpsH8dyDNG9jA3QvkWxg0Gi69Unv4iYiIiIhIxIRc+BljWgBnAJkcN1JorX0xzLmaF4cDJk+BceNh9WooKoLEROjTB1q0iHU6ERERERFp5kIq/Iwxk4GZQDrgO67ZAir8QpGYCEOGxDqFiIiIiIicYkKd4/cXYBbQ3lrrPu6hexRFRERERESasFBv9cwBLrLW7otgFhEREREREYmAUEf8lgPdIhlEREREREREIqPOET9jTIdjPr0P+JMx5l4gF6g4tq+1dndE0omIiIiIiMhJC3ar504CC7ccZYA3ajlmAWf4o4mIiIiIiEg4BCv8zolaChEREREREYmYOgs/a+1/ohlEREREREREIiOkxV2MMdONMZfVcvwyY8z3wh9LREREREREwiXUVT3vBA7WcjwfuCt8cURERERERCTcQi38coCNtRzfXNUmIiIiIiIiTVSohV8B0LWW492B4vDFERERERERkXALtfB7n8A+fllHD1Tt8/cg8F4kgomIiIiIiEh4hFr4/QxIATYZY5YbY5YTuPUzpapNREREREREmqhg+/hVs9buN8YMBq4GhlQdfhz4p7W2NFLhRERERERE5OTVW/gZY9zAi8AvrbXPAs9GPJWIiIiIiIiETb23elprK4HzAH/k44iIiIiIiEi4hTrH710CxZ+IiIiIiIjEmZDm+AGfAb8xxgwClgFHjm201v4j3MFEREREREQkPEIt/B6p+vP6qsexLKDCT0REREREpIkKdVXPUG8JFZFwqqiAkhJITAw8REREREQaIdQRPxGJpp07KZm/kP0rV1HmdJPgraR13x6knXM2dO8e63QiIiIiEmdCLvyMMRnAZKAL4Dm2zVr72zDnEjll2a+/ZtNLr7Osc3/2j78Sn8eDw1tJq22bGPrUy/S6aAKus0bHOqaIiIiIxJGQCj9jzHBgDmCAdGA/0A4oAfIAFX4i4XDgANteeo15wyZTmtG6+rDf5Sa/ex/mt++I46236dOpI3TpEsOgIiIiIhJPQp279yfgdaANUAqMJjDy9yVwZ2SiiZx6KpYuJbddtxpFX432lFS+zOlPycIlUU4mIiIiIvEs1MJvEPCQtdZPYCN3j7V2J4Gi74FIhRM51Rxc/hV5XXoH7bM/pw8Hv/wG/P4opRIRERGReBfqHD8fUFn18T6gE7AWyCcw8iciYeAvLaMyMSloH5/HgxfA6wWPJ2hf9u2Dz5bAhm+gYw94bSaMHAPZ2WHLLCIiIiJNX6iF31cERv02EtjM/W5jjAO4AVgXoWwipxx3RkuSCg9RFKT4SyguJCHRA2533SeyFubNhQ9fhQ5O6JQBbj/sWgJ//RCGT4apl4JDO7WIiIiInApC/anvfggMMgC/IrCwy/vAGOCWCOQSOSW1Hn0GOZtXB+2TvWkVrUefAcbU3enTT+Djf8K4TjAwB9q2AJcT+nWC8TmQ+x7MnRPW7CIiIiLSdIVU+Flr51lr36z6eKu1th+BhV7aW2sXRTKgyKnEMWQwA20B7TatqbW9xa5tDD24Fc+okXWfxOeDOa/DGdmQVMutoG4XjMiB+W9DaWl4gouIiIhIk9ag+7yMMS2MMUONMR5r7UFrrY1UMJFTUlISbX90PZP2rWHIJ3NouXMLiQUHSc/bwelLP2LKxs/oeOO1kJFR9zk2bgRTCC1Tg7yOB9K98M034X8PIiIiItLkhLqPXzLwBHANYIGewGZjzJPATmvt7yIXUeQU07Ytbe+6jbO/+ooBS1dQsbsQV0oKGecMwjFkMCQFX/yFggJICeF3OikGDh8OT2YRERERadJCXdzlPmAgMJbA3L6j5gC/BlT4iYSTx4MZNoyMYcMa/tyEBKgMYTC+wtZfRIqIiIhIsxBq4XcJcI21drEx5tifKFcD3cIfS0QarUcPKHBAeSUk1LHyp88P+Rb69IluNhERERGJiVDn+LUHdtRy3E3oxaOIRENKCpxxDny5LbCtQ22+2QG9h0GbNtHNJiIiIiIxEWrhtxYYXcvxi4Hc8MURkbC46BJI7w+LNsD+gv8WgIeK4dMNUJEFl18T24wiIiIiEjWhjtY9CDxmjEkFDDDOGHMjgT38LotUOBFpJI8HfnAzfPYZ/Od9WL4ZBmbD+jI4+2oYNUrz+0REREROISEVftbamcaYROAeIBn4O4FbP6+31r4dwXwi0lhuN4wZA2edBcXFsGwZXHkNOBq0i4uIiIiINAMhz8+z1j4PPG+MaQM4rLX7IpZKRMLHGEhLCxR8KvpERERETkkN/inQWpsP9DbGXGKMaRWBTCIiIiIiIhJGQUf8jDE3Ay2P3aDdGPMWcAGBuX4HjTFnWWvXRjamiIiIiIiINFZ9I37fA7Yf/cQYczEwper4cGAjcHfE0omIiIiIiMhJq2+OX3fgy2M+nwLMtta+AmCM+QXwbISyiYiIiIiISBjUN+KXBBQe8/kIYOExn28A2oU7lIiIiIiIiIRPfYXfTmAAgDEmA+gHfHpMe1tqFoZ1Msbca4yxtTx6HNPnTGPMJ8aYMmNMnjHm98YYZ8PekoiIiIiIiByrvls9/wU8aozpBEwmsHff58e0DwPWNeD1tgIjjzu2H6DqNT4EXgduAHoCzxFYROauBryGiIiIiIiIHKO+wu9+oFPVn3nA1dZa/zHtVwLvNuD1fNbaPXW0/YjA6OF1Va+xyhiTDfzRGHOftfZIA15HREREREREqgQt/Ky1ZcD0IO3jGvh6HY0xO6s+/hq4z1r7SdXno4G5xxWWc4C/AYOBxQ18LREREREREQGMtTY6L2TMFKAFsBpIB35IYMRwsrX2Q2PMeuA1a+3dxzwnBSgGLrPWvlrHeX8A/AAgMzNz6MyZMyP6PoqLi0lNTY3oa4hEgq5diVe6diVe6dqVeKVrN76dc845K6y1w44/Xt+tnmFjrX3vuEOLjDEdgZ8SmNvX2PM+DTwNMGzYMDtu3LhGZwzFggULiPRriESCrl2JV7p2JV7p2pV4pWu3eapvVc9I+xTIqfo4D2h/XHvmMW0iIiIiIiLSCLEu/IYQWCkUYAkw0RhzbKbJQAk1N5EXERERERGRBoha4WeMmWGMGW+M6WaMGWSMeQyYCDxc1eUJAnMAnzHG9DPGXATcB/xVK3qKiIiIiIg0XtTm+AFZwIsENn0vAL4CJlhrPwaw1u4wxkwCZgArgMME5u79MooZRUREREREmp1oLu5yZQh9PgNGRSGOiIiIiIjIKSPWc/xEREREREQkwlT4iYiIiIiINHMq/ERERERERJo5FX4iIiIiIiLNnAo/ERERERGRZi6a2znEXGFhIfv27aOysrLR52jRogVr1qwJYyqR6NC1e+pwu920a9eO9PT0WEcRERGRJuKUKfwKCwvZu3cv2dnZJCUlYYxp1HmKiopIS0sLczqRyNO1e2qw1lJaWsquXbsAVPyJiIgIcArd6rlv3z6ys7NJTk5udNEnItLUGWNITk4mOzubffv2xTqOiIiINBGnTOFXWVlJUlJSrGOIiERFUlLSSd3WLiIiIs3LKVP4ARrpE5FThr7fiYiIyLFOqcJPRERERETkVKTCT0REREREpJlT4SciIiIiItLMqfBriNJSzNKlHJjxV/bc+wAHZvwV/5JPoLQ04i/93nvvMWjQIBISEsjJyWHGjBmNOo8xpvrhdDrJysriu9/9Lrt37w5r3h49enDvvfcG7ZOTk1MjT22PcHrssccYP348GRkZGGNYvnz5SZ+zuLiYW265hU6dOpGUlESPHj343e9+h9/vr+5TVFTE7bffTteuXUlMTGTgwIG89dZb9Z57/fr1TJ06lbZt25KamsrQoUN5/fXXa/Sx1nL//ffTuXNnEhMTGTp0KPPnz2/w+3jyySdr/L23b9+eiy++WPv+HePw4cNce+21tGrVipSUFC688EK2bdsW9DlLly7l7LPPpl27diQkJNClSxduvfVWioqKqvsUFxczffp0Bg4ciMfjoX///iec5/h/n2Mf77zzTtjfq4iIiDQ/KvxCtWcP+x6cwaJFq3izbT9mDZzEm2378fHHK9n34AzYsydiL718+XIuvvhizjvvPFauXMm9997L3XffzZNPPtmo8/3tb38jLy+P7du3869//Yvc3FyuueaaMKeu37Jly8jLyyMvL4/PP/8cgLfeeqv6WF5eXlhfr6SkhIkTJ/L73/8+bOf8yU9+wttvv83zzz/PmjVr+N3vfscDDzzAI488Ut1n+vTpzJ49m2effZZVq1Yxbdo0Lr30UhYsWBD03FOmTKG4uJi5c+eSm5vLlClTuOyyy1ixYkV1nwcffJAHHniAP/zhD3zxxReMGTOGKVOmsHbt2ga/l+TkZPLy8ti9ezevv/4627dvZ/LkyRw5cqTB52qIeFl58oorrmDJkiW8+eabLFy4kCNHjjBp0iTKy8vrfE5SUhLXX3898+bNY/369Tz11FO8/fbb/OAHP6ju4/V6SUxM5KabbuLSSy+t9TzTpk2r8XWRl5fHTTfdROvWrZk4cWLY36uIiIg0Q9baZvMYOnSorcvq1avrbKtXSYnde8/v7AtPz7Z/mP2VnTF3XY3HC8/Mtnvv+Z21JSWNf40grrzySjty5Mgax+644w7bpUuXBp8LsC+99FKNY48++qhNT0+vcWz58uV24sSJNiUlxbZp08ZOnTrVbt26tbp9x44d9pJLLrGtW7e2CQkJtmvXrvaPf/yjtdbasWPHWqDGY8uWLUFzbdmyxQJ20aJFJ7SVlZXZ2267zWZlZVm322379+9v//3vf1e3l5aWWsA+9thj9qKLLrJJSUk2OzvbPv7447W+1po1ayxgly1bFjRTKHr37m3vvvvuGsemTJlir7jiCmuttYWFhdYYY994440afSZNmmQnTZpU53nz8vIsYOfOnVvjeHJysn3yySettdZ6vV7bpk0b+5vf/KZGn/79+9sf/vCHJ5yzsLCwztd74oknbEpKSo1j8+bNs4BdsGCBtdba8vJye/fdd9vOnTvbxMRE269fP/vcc8/VeM6f/vQne/rpp9vk5GSblZVlr776art3797q9vfff98Cds6cOXbEiBHW4/HY5557zh48eNB+97vfte3atbMej8d27tzZ3nXXXdXPC/UaePrpp+0VV1xhU1JSbKdOneyf//znOt9zQ+Tm5lrA/uc//6k+tmfPHut0Ou0///nPBp3rD3/4g23fvn2tbXfeeaft169fveeoqKiwmZmZ9rbbbgva76S+7zUh8+fPj3UEkUbRtSvxStdufAOW21pqJY34hcD/xZd85czgQE7PWtsPdOnJV84M/F+ujMjrL1myhMmTJ9c4NnnyZLZt28bOnTsB2Lp1K8YYnn/++QadOy8vj1mzZjFq1KjqY6tXr2bs2LGMHDmS5cuX8/HHH+N0Opk4cSJlZWUA3HTTTRQUFDBv3jzWrl3Ls88+S8eOHQGYNWsWOTk53H777dWjE506dWr0+7/jjjt46aWX+Nvf/sbXX3/NpZdeyuWXX87ixYtr9Pv1r3/N5MmTWblyJbfeeis333wzH3zwQYNea/r06aSmpgZ9HHu75VlnncXs2bOrb/lbvnw5S5cu5fzzzwegvLwcay2JiYk1XicpKYnFixcT+No8Ubt27ejVqxcvvfQSRUVF+Hw+XnjhBay1nHPOOUDgVtD8/Pxar43j/24a4+i+l0dH5L73ve/x/vvv89xzz7F69WruvvtubrnlFl555ZXq5xhjePjhh/nmm2949dVXWb9+fa2jybfffju/+tWvWLt2Leeeey533nkna9asYfbs2axfv55XXnmFnj3/+/UW6jVwzz33MGnSJHJzc7n11lu54447WLJkSXX7XXfddcK/RSiWLFlCSkoKY8aMqT6WmZnJ4MGDG/R3vW3bNmbNmlX9b9hYb775Jnv37q0xcigiIiISVG3VYLw+IjXil/+XR+3fX/zIzpi7rtYRvxlz19m/vzDP5v/l0Ua/RjBut9s+9dRTNY598803FrCff/65tdbanTt32t69e9tZs2YFPRdgExISbEpKik1KSrKAHTRokN2zZ091n2nTptnLL7+8xvPKyspsUlJS9cjVgAED7D333FPn63Tv3j1o+/HqGvE7dOiQdblc9tlnn61xfPLkyfa8886z1v53tOf666+v0Wfq1Kl2woQJJ7xWsBG/PXv22A0bNgR9FBUVVfcvKyuzN9xwgwWsy+WyDofDPvjggzXOOWzYMDtq1Ci7bds26/P57FtvvWU9Ho8F7MGDB+v8O9m5c6cdOXKkBazT6bQtW7a0H374YXX7Rx99ZAG7bdu2Gs/785//bFu1anXC+Roy4rdnzx47adIkm5GRYQ8ePFj9d7Z58+Yaz/v5z39uzzzzzDrP+8knn1jA5ufnW2v/O+J37GidtYER0NpGKa1t2DXw05/+tEafnJwce++991Z//pe//MUOGDCgzrx1+dWvfmW7du16wvELLrjAXnLJJfU+f8iQITYxMdEC9tvf/rYtKyurtV+oI37f+ta37Lhx4+rtpxE/kdjStSvxStdufEMjfo1XWVhEeWp60D7lqelUFBVHKdGJsrOzWbt2LVOnTq237/3338/KlSvJzc1l3rx5JCQkMHXqVHw+HxCYe/fGG2/UGOVq3bo1ZWVlbNiwAYBbb72VBx54gDPPPJM777yThQsXRuR9rV+/Hq/Xy9lnn13j+NixY1m1alWNYyNHjqzx+ejRo0/oU5/MzEx69OgR9JGamlrd/+GHH2b+/Pm8+eabfPHFFzz99NPcf//9vPDCC9V9Zs6cidvtJicnB4/Hwy9/+Uu+//3vA+Bw1P4l6Pf7ufHGG0lLS2PhwoUsW7aMG2+8ke985zt8/fXXDXpPoTpy5AipqamkpKTQvn17du3axaxZs8jIyGDZsmUAnH766TWuixkzZlRfEwDz5s1j4sSJdOrUibS0NCZMmABwwiIoZ5xxRo3Pb775Zl588UUGDhzIbbfdxty5c6tHQxtyDQwaNKjG5x06dGDv3r3Vn992223k5ubW+XdQXl5e4/2F8vUUijfeeIMvvviCV199ldzcXG655ZZGn2vjxo18/PHH/PCHPwxLNhERETk1uGIdIB6409NIKC4MWvwlFBfiSUuts/1kZGVlsee4xWOO/jCblZXV4PMdLW4AevbsSUpKCiNHjmT+/PlMmDABv9/PNddcw1133XXCc1u3bg3Atddey+TJk5kzZw7z58/nvPPOY+rUqbz88ssNztOUTJ8+nddeey1onxdeeIFLL72UwsJCfvnLX/Laa69x8cUXA4HCaPPmzdx3331MmzYNgO7du7NgwQJKSko4fPgwHTp04JZbbqFNmza0aNGi1teYM2cO7777LgcOHCAjIwOAwYMHs3DhQh555BH+/ve/V//b79mzh86dO1c/d+/evY26LpKTk1m5ciXGGDIzM2sUuH6/H2MMy5Ytw+1213je0eJ148aN/5+9e4+rKfv/B/7a3c7pdKWrQhcRRaIwiolyv8uMewYTPi4/fKpxjGFcpjEfgxhjxPiM64zCuA1GbtWEpEgRGaHwpYg0Sifd3r8/mvano06KMOX9fDz2Yzprr9veZzlz1llrr4UBAwbA19cXixcvhpGREW7evIn+/fujoKBAKY2Ojo7S64EDB+LOnTs4evQoIiIiMGLECHTo0AFhYWE1ugYtLS2l14IgKK2wWp30CQn/m7JdVs9GjRrh4cOHICKl1WYfPHhQoRNbmbL3p1WrVjAyMoKnpyc+//xzWFtbV7tuZTZs2ABjY2N4e3vXOC1jjDHG3l/c8auGBp1cYBWegMvmjVXGsbr9Jxp4ub6R8t3d3XH06FF8+eWXYlhYWBisrKzE5+peh7q6OgBA8fe2FK6urrh06RKaNWtW5ZYKjRo1woQJEzBhwgT069cPo0aNwrp166Cvrw8tLS1xBPF1tGjRAhoaGoiKihI7qwDwxx9/VFj2PiYmRhxJA4Do6Gg4ODjUqLxly5Zh/vz5VcYxNzcHUDo6VFRUVGHUTl1dvdJn92QyGWQyGRQKBfbt21flF/eylfQnRdYAACAASURBVDTL3pvK8m7RogWMjY1x9OhRpc7H0aNH0aVLlyqvoTKCICjd4/JcXV1BRLh37544iveic+fOobCwEKtXr4aGRulHS/nn617G2NgYY8aMwZgxYzB27Fh0794dN2/erFEbeF2q7oG7uzuePXuG06dPi8/5ZWZmIj4+Hn5+fjUqo6wjWva8bE0UFBRgy5YtmDhxYoVOLmOMMcZYVbjjVw1q7dvB6XgE7t9OQXqjphXOG6ddh1PxE6i1c64k9ev797//DTc3N3zxxRfw8fHBuXPn8P3332PVqlVinHv37sHLywvffPPNS6en/fXXX8jIyAAR4c6dO5DL5TA1NYW7uzsAYN68eejYsSPGjh2LWbNmwcTEBGlpadi/fz9mzZoFW1tbzJgxA/369YO9vT3y8/Oxd+9ecXofANjY2ODMmTO4c+cOZDIZGjZsqHJaY1UMDQ0xdepUzJ07Fw0aNICjoyNCQkJw9OjRCtNL9+7dCxcXF3h6euLgwYPYv38/Dh06JJ5PT0/HgwcPkJaWBqB0CqGGhgYsLCxgamoKoHQ01MzMrFp1MzExgZubG+RyOXR0dGBra4tz585hzZo1mDRpkhjvyJEjAAB7e3vcvn0bX3zxBQBgyZIlYpydO3di4cKFOH36NIyNjfHhhx/C0NAQ48aNw6JFi6Crq4udO3fi1KlTmDNnDoDSTqC/vz8CAwPRvHlztG3bFhs3bsT169cRGhpawztdNUdHR4wePRrjx4/Ht99+i06dOiEnJwfnz5/HX3/9BX9/f7Ro0QIlJSVYtWoVPvroI8THx1d76wy5XI7OnTvDwcEBRISQkBDo6+vD0tISMpms2m3gZYKCgrB169Yqp3tWxsnJCb1798akSZOwceNG6OjoYM6cOWjWrJnSv7eRI0dCT08PGzduBFC6/56pqSkcHBygpaWFS5cuYc6cOejUqRNatmwpprty5QoKCwvx8OFD5Ofni6OOrVu3FjvRALBnzx48fvyYF3VhjDHGWM1V9uBfXT3e2HYORETp6fRgYSD9/uVq+u/WE/TDnlj679YTdHzJD6VbOaSnv17+L3Ho0CFycnISl7pfuXKl0vmyxVE2b95cZT4ot8WCIAhkampKAwcOpMTERKV4ly5dokGDBpGhoSFJpVJq1qwZTZo0iR4/fkxERNOmTaPmzZuTVCqlhg0bUr9+/SgpKUlMHxcXR+3atRMXtKiN7RzMzc2rXMp/7dq11L9/f9LW1iYLCwv6/vvvlfKRy+UVtpkAQN98802VdatKeno6TZgwgSwtLcX7tGDBAqXFO3bs2EHNmjUjLS0tMjY2Jh8fH7p7965SPsHBwQSA0su1o7i4OOrduzcZGRmRjo4OtWvXrsJWHCUlJfTVV19R48aNSUtLi9q3b08nT56stK413c7hRYWFhRQYGEjNmzcnTU1NMjY2pm7duiltVREUFCTeCw8PDzp48CABoLNnzxLR/xZ3yczMVMp7/vz55ODgQDKZjAwMDKh79+5iGqLqt4Hdu3cr5evu7q60aIxcLieJRFLldary5MkT+uSTT8jQ0JBkMhn179+/Qrvu1KkT9e7dW3y9fv16cnJyIl1dXZLJZOL2H0+ePFFKZ2ZmVmnbTH/hc8XDw6PSBYtU4cVdGHu3uO2yuorbbt0GFYu7CKRiOfm6yNXVlc6fP1/pueTkZLRq1er1ClAokBsdjeeJV1GQkwstPV00+MC1dKTv76Xv2duXn58PbW1t7N69Gx999NG7rs4/Vk5Ojjgiy94PtfK59w8QGRmJbt26vetqMFZj3HZZXcVtt24TBOECEVV4Bo2netaEtjaoY0cYeXm965owxhhjjDHGWLXxdg6MMcYYY4wxVs/xiB+r86RSaaWraDLGGGOMMcZK8YgfY4wxxhhjjNVz3PFjjDHGGGOMsXqOO36MMcYYY4wxVs9xx48xxhhjjDHG6jnu+DHGGGOMMcZYPccdP8YYY4wxxhir57jjxxhjjDHGGGP1HHf86oCoqCgMHjwYVlZWEAQBgYGBr5RPWloaBEEQDw0NDTRp0gTTpk1DTk5OrdZZQ0MDW7ZsqTJO+bpUdlhbW9dqnRYtWgR3d3fo6elBEAQ8evTotfPMyMjAuHHj0KhRI8hkMjg6OmL9+vVKcR48eIBPP/0UjRs3hra2Njp37ozTp0+/NO+IiAh07doV+vr6MDIywsSJE5Gdna0UZ/jw4bC1tYW2tjaMjY3Ru3dvxMXF1fg6Ro4cqdQurK2tMWPGDDx58qTGedUHBw4cQJs2bSCRSGBra4u1a9e+NM0HH3xQoQ3b2dmJ558/f465c+fC2dkZurq6MDExQb9+/XDhwoVaKZ8xxhhjrCrc8asJhQJq52KAVV8Diz8r/e+ZM4BC8UaLzc3NhYODA7799luYm5u/dn4HDhxAeno60tLSsHHjRhw4cACzZ8+uhZrWTHp6unjs2bMHABAfHy+GvUoHpirPnz+Ht7c35HJ5reU5ZswYJCUlYe/evbhy5QqmTZuG6dOnY9++fQCA4uJiDBo0CFevXsWvv/6KxMREdO3aFb169cKff/6pMt/4+Hj07t0bHh4euHDhAg4ePIhLly7ho48+Uorn7u6Obdu2ITk5GSdPnoSJiQl69OiBhw8f1vhaevToIbaLoKAg7NixA76+vjXOpyaKi4tRUlLyRsuoqTNnzmDYsGEYOnQoEhMT8fnnn8PPz++lP2QAwIQJE5TadUxMjHguNzcXFy5cgFwuR2xsLMLDw2FgYAAvLy/cuXOnVspnjDHGGFOJiOrN4eLiQqpcvXpV5blqSU8nWhhAzxeNI9rxGdFvC0r/G/gp0cKA0vNvgZWVFX311VevlDY1NZUA0KlTp5TC/fz8yMnJSSns2LFj5ObmRlKplCwsLGj8+PH06NEj8XxSUhL16tWLDAwMSCaTUcuWLWnbtm1iHQEoHS8TERFBAOju3bsVzj158oQmTpxIRkZGJJFIqGPHjhQeHi6eT05OJgAUEhJCHh4eJJFIqFmzZrRnz55Kyzpy5AgBoMzMzJfW62WkUin9+OOPSmEODg40d+5cIiK6fPkyAaCLFy8qxbG3t6fJkyerzNff35/atm2rFBYdHU0AKDo6WmW6jIwMAkBhYWEVzj19+lRluhEjRlD//v2VwubPn09aWlpUUFBARET37t2jMWPGkJGREenp6VGXLl3ozJkzYvyCggKaOHEi2djYkFQqJVtbW/ryyy/F9EREcrmcHB0dafv27dS8eXNSV1enW7duUUJCAnl5eZG+vj7JZDJq1aoVhYaGiunu3r1LH330Eenr65O2tjZ5enpSQkKCeL7sPQ0PDxfbraOjIx0/flzlNasydOhQ6t69u1LYjBkzyN7evsp0nTp1ounTp9eorIKCApLJZEpt6FXLr8xrf+79Q0RERLzrKjD2SrjtsrqK227dBuA8VdJX4hG/6lAogPUrgaaFoA9sAPMGgK526X/dmgFWRaXn3/DIX1UiIyMhCAIiIyNrlO7WrVsICwuDm5ubGBYeHo7Bgwdj5MiRuHTpEvbv34+0tDR4e3ujtC0Bo0aNgpGREaKjo3H58mUEBQWhQYMGAIC4uDioq6tj9erV4sjH6/Dx8UFkZCRCQ0MRHx+P9u3bo2/fvrh165ZSvICAAEydOhWJiYnw9vbG8OHDcfXq1RqV5enpCV1d3SqP8iOR7u7u2L17NzIzM0FEOHr0KFJTU9GnTx8AQH5+PgBAKpUqlSOVShEVFaWyHvn5+RXSaGtrA4DKdAqFAsHBwTAwMEC7du1qdN2V0dbWRnFxMYqLi5GbmwsPDw8UFxfj2LFjuHDhAjw9PeHl5YWbN28CKB29s7S0RGhoKJKTk7FixQqsW7cOK1asUMo3NTUVmzdvxi+//IKkpCSYmpri448/RuPGjRETE4PLly9j+fLl0NfXBwCUlJRgwIABSE1NRVhYGGJiYqCvr48ePXpUmPoaEBCARYsWITExEW3btsXw4cOVpjF/8MEH4nujypkzZyrE6dOnD/7888+XTg/euXMnjI2NYW9vj08//RT37t2rMv6zZ89QVFQEHR2dWimfMcYYY0ylynqDdfV4YyN+p0+XjuyFL6X8sIVE4UsrHl9/SlRu9ONNUTXid+7cObK3t6dz586pTFs24qetrU06OjokkUgIAHl5eVFeXp4Yz8PDg+RyuVLa27dvK41c6evr0+bNm1WWpa6uXuX5F6ka8UtKSiIAdPLkSTGspKSEHBwcaOrUqUT0vxG/wMBApbTt27cnX1/fCmVVNeJ39+5dSklJqfLIz88X42dnZ9PAgQMJAGloaJBUKqXt27eL5xUKBTVu3JiGDBlCmZmZVFBQQOvXrycApK+vr/J+HDp0iARBoM2bN1NhYSE9ePCA+vXrRwBo5syZSnFXrlxJOjo6JAgCWVlZUXx8fKV51mTELzExkZo2bUoeHh5ERBQcHEw2NjZUXFyslK5z584V2kp5S5cupdatW4uv5XI5qaur0/3798WwkpISkkgkFBISUmkeZfciJSVFDHv27BkZGRnRsmXLiOh/7+nhw4fFOGlpaQSAIiMjla6zsjZRpri4mADQ1q1blcLPnz9PAOjSpUsq0/7www905MgRunTpEh04cIDat29PjRo1qnJkecyYMWRnZyf++3ud8ivDI36MvVvcdlldxW23boOKET+Nt97TrItiIwHbhlXHsW4AnIsAyo2cvU0dO3bEtWvXqhV38+bNcHFxQXFxMVJSUuDv74/x48dj586dAEpH7GJiYipdUCIlJQXOzs4ICAiAr68vtmzZgm7dumHQoEFo3759rV4TAFy5cgVqamro0qWLGCYIArp27YorV64oxe3cubPSazc3t0oXzqhK48aNaxR/3rx5uH//Po4dOwYzMzMcP34cU6ZMgZmZGXr27AmpVIr9+/dj0qRJMDExgbq6Otzc3DBy5EiEhYWpzLd///5YuXIlZs+eDV9fX2hoaEAulyM6OhpqasoD9RMnTsSgQYOQkZGB4OBgeHt7Izo6Go0aNarRtYSFhUFXVxdFRUUoLCxEr169xIVq4uLicOfOHXEUrszz58+Vylm3bh02b96M27dvIy8vD0VFRdDS0lJK06RJE6U0giAgICAAPj4++PHHH9GtWzcMHjwYbdu2BVDaBiwsLJQWSpHJZHB1da3QBpydncW/LSwsAJQurlMmNDS0RvekJqZNmyb+3aZNG7i5ucHa2hrbtm2Dn59fhfizZ8/G8ePHERkZKY7mMsYYY4y9KTzVszqeZgO60qrj6GoDOX+9nfq8JktLS9jZ2cHe3h4DBgzA4sWLsWvXLty4cQNA6dQ6uVyOhIQEpSMlJQV9+/YFACxYsADXr1/H8OHDkZSUhA8++ADz589/l5dVK2oy1fPq1atYt24dfvrpJ/Ts2RNOTk7w9/fHkCFDsGzZMjFPFxcXxMfHIzs7G/fv30dUVBSePXuGZs2aVVmXf//733jy5Anu3r2LrKwszJo1C9nZ2RXSGRoaws7ODl26dMHPP/8MIsKGDRtqfO0ffvghEhIScO3aNSgUChw5cgRWVlYAStuEs7NzhTaRnJws/kCwfft2+Pn5wcfHB0eOHMHFixchl8tRUFCgVE75aY1lAgMDkZycDG9vb1y8eBEdOnTAV199VeNrKN/JFARBrHt1qampwdTUFBkZGUrhZZ3HmnSmjY2N0bx5c6SlpSmFl5SUYMqUKdi5cyciIiLQqlWrN1I+Y4wxxlh53PGrDn1DIDe/6ji5CkDP4O3Up5apq6sDKH1GDIA4kmJnZ1fh0NXVFdPZ2tpi2rRp+PXXX7FkyRIEBweL57S0tFBcXPzadXN0dERJSYnS9gdEhFOnTqF169ZKccuvoAgA0dHRcHBwqFF527Ztq9C5efFwcnICUPp8FoAKI3Dq6uris5DlGRgYwNTUFA8ePMDJkycxbNiwl9ZHEARxq4gdO3ZAQ0MDgwYNqjINEYnPFtaETCaDnZ0drK2tK4zSubq6IiUlBQ0bNqzQJso6I1FRUejUqRNmzpwJFxcXNG/eHKmpqdUu387ODjNmzMC+ffswb948cbTR0dER9+/fF3+YAIC8vDycP3++QhuoDe7u7jh69KhSWFhYGOzt7WFsbFztfP766y/cvHkTTZo0EcOKioowduxY/P777/jjjz8qbZ+1VT5jjDHGWHnc8auOjt2AW1lVx0l7AnTq/kaKz83NFTsdBQUFyMjIQEJCgtIX4djYWLRs2RKxsbEvzS8rKwsZGRm4d+8eIiMjsWTJEtjb24sjD0uWLMGBAwfg5+eHhIQE3Lx5E2FhYfj000+hUCiQm5uL6dOnIzw8HKmpqbh48SLCwsKUvsTa2NggIiIC9+/ff60FKRwdHTFw4EBMnjwZJ06cQHJyMqZNm4abN2/C399fKW5wcDB27dqF69evY+7cuUhISFDapuL27dtISEgQF4VJSkpCQkKC0gIhjRs3rrTDW/6QSCQAACcnJ9jY2GDq1Kk4e/Ysbt26hQ0bNiA0NBTe3t5inmUjO6mpqfj999/h4eEBOzs7pboFBQWJUxuB0lGhFStW4PLly+JCKf7+/li0aBGaNm0KoHTLh6CgIMTHx+POnTuIjY3FuHHjkJGRgVGjRr3yPa/MJ598AnNzcwwYMAAnT55EWloaYmJiEBgYiMOHDwMA7O3tER8fj8OHD+PGjRtYsWIFDh069NK8s7KyMHPmTERERCAtLQ0XLlzA8ePHxfbUt29fODk5YdSoUTh79iwuX76MMWPGQBAETJ48uUbXMXLkSEyaNKnKOP7+/vjjjz+wePFi/Pnnn/jpp5+wYcMGzJ07V4xz+vRptGzZEomJiQCA5ORkLFmyBHFxcbh9+zYiIyMxYMAASCQS+Pj4AAAKCgowbNgwnDhxArt27YK+vj4yMjKQkZEh/ohQ3fIZY4wxxmqssgf/6urxxhZ3ycsr3bJh0+zKF3fZNKv0fLkFUmpT2cInLx5lC2+Uj1PVw7hli7uUHWpqamRhYUGjR4+m1NRUpbhRUVHk5eVFurq64nYNs2bNosLCQlIoFDRq1CiytrYmiURCJiYmNHz4cLpz546Y/siRI9SyZUvS1NSs1e0ctLS0VG7nsGPHDurSpQtJJBKytbWlXbt2KeUzYsSISu+jqkVFquP69es0bNgwMjMzI21tbWrVqhWtWrWKSkpKxDhBQUHUpEkT0tTUpEaNGtH06dMpKytLKR+5XE4SiUR8XVxcTN26dSMDAwOSSCTUtm3bCovlJCcnU8+ePcnExIQ0NTXJ0tKShg4dSrGxsZXWtabbObzo4cOH5OvrS+bm5mJ5w4YNExccyc/PpwkTJpChoSHp6+uTj48PrVy5Uum6yrZzKC8nJ4dGjBhBVlZWpKWlRaampjR69GilBWDu3r1Lw4YNE7dz6N69u9IWGZUt2FNYWFjh/e3UqRP17t27yuskItq3bx+1bt2aNDU1ydramtasWaN0vqy8s2fPEhHRzZs36cMPPyQjIyPS1NQkKysrGjduHN26dUtMU9ZOKzu++eabGpVfXby4C2PvFrddVldx263boGJxF4EqmZJWV7m6utL58+crPZecnKz0LE2NZWQA61eiQMiEVnOT0mf6chWlI30FDYB/+QO1sLk6q7lr166hVatWiIuLg6ur67uuzj9WTk4O9PT03nU12Fv02p97/xCRkZHo1q3bu64GYzXGbZfVVdx26zZBEC4QUYUvxbyqZ3WZmwPyJSiMPgOtS+eAG38BeoZAt6FAu3YAr8rHGGOMMcYY+4fijl9NaGujpGMnwKvHu64JY4wxxhhjjFUbd/xYndeyZctKV9FkjDHGGGOMleJVPRljjDHGGGOsnuOOH2OMMcYYY4zVc9zxY4wxxhhjjLF6jjt+jDHGGGOMMVbPccePMcYYY4wxxuo57vgxxhhjjDHGWD3HHT/GGGOMMcYYq+e441cHLF++HJ07d0aDBg1gaGiILl26ICwsrMb5pKWlQRAE8dDQ0ECTJk0wbdo05OTk1GqdNTQ0sGXLlirjlK9LZYe1tXWt1mnRokVwd3eHnp4eBEHAo0ePXjvPmzdv4uOPP4aZmRl0dHTQq1cvXLlypUK8jRs3onnz5pBIJHBwcMCuXbtemndBQQH8/PxgZmYGbW1teHh4IDExUWX89evXQxAEDBgwoMbXUZa27DA3N8fgwYORnJxc47zqg6tXr6JHjx6QyWQwMTHBjBkzoFAoqkxz8uRJDBo0CE2aNIEgCFixYkWFOC/e57Lj9OnTYpywsLBK4/z888+1fp2MMcYYe39wx68mFAoI52KQ9d1/8ODrucj67j8oiT4DvOQL4esKDw/HxIkTERERgdjYWLi5uWHAgAE4c+bMK+V34MABpKenIy0tDRs3bsSBAwcwe/bsWq71y6Wnp4vHnj17AADx8fFiWFxcXK2W9/z5c3h7e0Mul9dKfjk5OejRowfy8/Nx7NgxxMXFwczMDJ6ennj8+LEYLzQ0FFOnTsWsWbOQmJgIHx8fjBo1CuHh4VXmP3PmTPzyyy/YvHkzYmNjYWFhAS8vr0o7rAkJCViyZAnc3Nxe+XpkMhnS09Nx//597NmzB3fu3EGfPn3w7NmzV86zOgoLC99o/jWVnZ0NT09P6Orq4uzZs9ixYwf279+PqVOnVpkuNzcXbdq0QVBQEBo2bKgyXtl9Ln906tSpQrwrV64oxfnoo49e+9oYY4wx9h4jonpzuLi4kCpXr15Vea5a0tPp4RI5Hft2Cm07vIQ2Ri2nbYeXUPiq6fRwiZwoPf318q+hNm3akJ+fX43SpKamEgA6deqUUrifnx85OTkphR07dozc3NxIKpWShYUFjR8/nh49eiSeT0pKol69epGBgQHJZDJq2bIlbdu2jYiIrKysCIDS8TIREREEgO7evVvh3JMnT2jixIlkZGREEomEOnbsSOHh4eL55ORkAkAhISHk4eFBEomEmjVrRnv27Km0rCNHjhAAyszMfGm9qnLw4EESBIGysrLEsIKCAtLV1aWlS5eKYe3ataMJEyYopR0wYAD17t1bZd6PHj0iDQ0N2rp1q1LeDRs2pG+++UYp7tOnT6lFixa0d+9eGjFiBPXv37/SPJ8+faqyvODgYNLR0VEKO3HiBAGgyMhIIiJ6/vw5zZs3j5o2bUpSqZQcHR1p06ZNSmmWL19Obdq0IZlMRo0aNaIxY8bQgwcPxPNl9z4sLIw++OAD0tLSok2bNlFWVhaNHTuWTE1NSUtLi5o2bUpz584V0+Xn55Ofnx81atSINDU1qXXr1rRr1y7xvEKhIAD0448/0siRI0lHR4eaNGlCK1asUHnNqnz33Xeko6NDz549E8N+/fVXUlNTo3v37lUrDzMzM1q+fHmF8Mru84tqq30S1cLn3j9ERETEu64CY6+E2y6rq7jt1m0AzlMlfSUe8asOhQKZG1fjhIMGkj60R46FEQr0ZMixMEJyNwecdNBE5sbVb3zkr0xJSQmePn0KHR0dMSwyMhKCICAyMrJGed26dQthYWFKI0Xh4eEYPHgwRo4ciUuXLmH//v1IS0uDt7c3StsSMGrUKBgZGSE6OhqXL19GUFAQGjRoAACIi4uDuro6Vq9eLY5WvA4fHx9ERkYiNDQU8fHxaN++Pfr27Ytbt24pxQsICMDUqVORmJgIb29vDB8+HFevXq1RWWUjPVUdZSOR+fn5EAQBEolETK+hoQENDQ1ERUUBAJ49e4aEhAT06dNHqZw+ffrgzJkz4v18UWxsLIqKipTSaWpqwtPTU2laIABMmTIFPXr0wNChQ2t0rS+jra0N4H8jcuPGjcORI0ewadMmXL16FfPmzRNHJcsIgoDVq1cjKSkJu3fvxvXr1+Hj41Mhb39/fyxYsADXrl1D7969IZfLkZycjEOHDuH69ev45Zdf0Lx5czF+QEAAtm/fjrVr1+Ly5csYNmwYRowYUeFeLFy4EL169UJiYiJmz56NgIAApZHxuXPnQiqVVnndZ86cwYcffgiZTCaG9enTByUlJYiOjq7BHaycQqGAtbU1LCws4OnpiaNHj1Yar0OHDjAzM4O7uzt27Njx2uUyxhhj7P2m8a4rUBeUXIxHkl4unjRzAJ4/r3A+q1kjXLmbjA8TLkKt86tPtauupUuXIjs7G5MnTxbDZDIZ7O3tlb6sqtKrVy+oqamhqKgIz58/h5eXF4KCgsTzS5YswcyZM/H//t//E8O2bt0KKysrJCYmwtnZGbdv34afnx8cHBwAALa2tmJcExMTAICBgQHMzc1f61qvXLmCQ4cO4eTJk/D09AQArFu3DlFRUVixYgXWrVsnxp06dSpGjBgBAPj2229x8uRJrFq1Chs3bqx2edu2bUN+fn6VcZo0aQIA6NKlC3R0dODv74/ly5dDQ0MDX3/9NbKzs3H//n0AwIMHD0BEFe6Dubk5cnNzkZubCz09vQplpKenQ11dXbyX5dOV73xs2LABSUlJiI2NrfY1VseDBw+wePFiNGjQAC4uLrh27Rp27tyJW7duwcbGBgBgY2ODpKQkfP/99xgzZgyA0g5dGRsbG3z33Xdwc3PD48ePYWRkJJ5buHAh+vXrJ76+ffs2XF1d0aFDBwCAlZUVunTpAqB06uX69euxYcMGeHt7Ayh9XvPcuXNYunQpfv/9dzGfsWPHYsKECQAAPz8/fP/99zhx4gTc3d0BAKamprC3t6/y2tPT02FnZ6cUpqOjA11d3df+EcPR0RGbNm2Ck5MTFAoFQkJC0KdPH/z888/iPWzcuDGCg4Ph4uICADh06BA++eQTpKam4osvvnit8hljjDH2/uKOXzVkx53C/7UwqzLO3eYmyI6NQsM33PFbt24dli5dit9++w2NGzcWwzt27Ihr165VK4/NmzfDxcUFxcXFSElJgb+/P8aPH4+dO3cCKB2xi4mJwdq1ayukTUlJgbOzMwICAuDr64stW7agxIX27QAAIABJREFUW7duGDRoENq3b187F1nOlStXoKamJnYCgNJRpa5du1ZYRKVz585Kr93c3HDhwoUalVf+nr6Mubk5fv31V0ybNg36+vpQU1PDwIED0aNHj1pZOOZlkpKSMG/ePJw+ffqlo1jV8ezZM+jq6oKIkJeXB0dHR+zduxcNGjTAoUOHAABt2rRRSlNUVKQ08nzixAksW7YM165dQ3Z2NkpKSgCUduzKd/w6duyolM+MGTMwYsQInD17Fl5eXujTpw969uwJQRBw/fp1FBUV4cMPP1RK4+HhgeDgYKUwZ2dnpdcWFhZ48OCB+NrPzw9+fn41vTW1pmvXrujatav42s3NDQ8fPsSyZcvEjl/r1q3RunVrMU6HDh2Qn5+P5cuX4/PPP4eaGk/UYIwxxljNccevGgpzs/Fcz7jKOAV6MhQ+e1xlnNe1YsUKLFy4EL/99ht69OjxyvlYWlqKIxr29vbIzc3FqFGj8PXXX8POzg4lJSWQy+WVTtErG7lasGABxowZg7CwMISHh2Pp0qWYM2cOAgMDX7le/wSenp4vHT2LiIgQR6Z69eqFGzdu4PHjxxAEAQ0bNoSTkxNatGgBADAzM4MgCMjIyFDK48GDB+LU0co0atQIxcXFyMzMhKmpqVK6Ro0aAQBOnz6NrKwspc5YWUdLQ0MD586dE0eNqkMmkyEhIQGCIMDMzEypbiUlJRAEAXFxcdDU1FRKV9YRuXHjBgYMGABfX18sXrwYRkZGuHnzJvr374+CggKlNOU7iwAwcOBA3LlzB0ePHkVERARGjBiBDh061Hj1Wi0tLaXXgiCI96S6GjVqVOH9ysvLQ25urnjva5ObmxuOHDny0jjffvstsrOzq1w4hjHGGGNMFe74VYOmriEkOQoU6KmeRqmVkwdNHYM3Vocvv/wSq1atwu+//w4PD49azVtdXR0AxOXqXV1dceXKlQrT3V5ka2uLadOmYdq0afjPf/6D5cuXix0/LS0tFBcXv3bdHB0dUVJSgtOnT4tTPYkIp06dQrdu3ZTixsTEiHEAIDo6Gu3atatReTWZ6lle2WjW5cuXcfnyZXz++ecASjs4zs7OOHr0KIYPHy7GDwsLg7u7OwRBqLSMjh07QkNDA0ePHhU74IWFhQgPD0dAQAAAYMSIEUojoQDw2WefIS8vDz/88MNL378XCYKgMo2rqyuICPfu3VP5o8O5c+dQWFiI1atXQ0Oj9KOlJivPGhsbY8yYMRgzZgzGjh2L7t274+bNm2jRooX43GT5+v3xxx9KI2O1xd3dHV988QXy8vLEqdNhYWFQU1N7rVVTVYmPj6+0Tb0YR09PDwYGb+4zhjHGGGP1G3f8qsGwQ1c0jg1BsoWRyjhNUjJh2GnUGyl/9uzZ2LBhA0JCQmBvby+ORmhra4tfBGNjYzFu3Dhs27atwjS6F2VlZSEjI0Oc6rlkyRLY29ujVatWAEqf8evVqxf8/Pwwbtw46OnpISUlBbt378batWtRXFwMuVyOYcOGwcbGBtnZ2QgLCxOf9wNKn++KiIhA3759oaWlBWPjqkdMVXF0dMTAgQMxefJkrF+/HpaWllizZg1u3ryJgwcPKsUNDg6GnZ0dnJ2dsWnTJiQkJGDr1q3i+du3b+PJkyfiojBJSUkwNDSEtbU1DA0NAdRsqicA/PTTT3BwcICpqSkuXLiA2bNno1evXuKzhgAwZ84c+Pj4wMXFBV5eXti/fz9+//13HDt2TIyzc+dOLFy4EKdPn4axsTGMjIzw6aef4rPPPoOxsTGaNGmCpUuXAgB8fX0BAA0aNBAX1CljYGAAdXX1Wu8QOTo6YvTo0Rg/fjy+/fZbdOrUCTk5OTh//jz++usv+Pv7o0WLFigpKcGqVavw0UcfIT4+Ht9880218pfL5ejcuTMcHBxARAgJCYG+vj4sLS0hk8kwdepUzJ07Fw0aNICjoyNCQkJw9OhRcRGd6goKCsLWrVur3A9x3LhxWLp0KcaOHYtFixbh4cOHmD17Nnx8fGBhYQGgdI9FJycn+Pv7Y9KkSQBKt/e4efMmgNIpsPfv30dCQgL09fXFZ2Dnz5+PLl26oHnz5lAoFAgNDcX27duVnkNdvnw5mjdvLv57PHz4MJYtW4bPPvtM/JGGMcYYY6zGKlvqs64eb2w7h7w8erhETiG/zqegmCD64eIPSkfo7vmlWzrk5b16GVXAC1sjlB2ffPKJGKdsO4Sqlt8t286h7FBTUyMLCwsaPXo0paamKsWNiooiLy8v0tXVFbdrmDVrFhUWFpJCoaBRo0aRtbU1SSQSMjExoeHDh9OdO3fE9EeOHKGWLVuSpqZmrW7noKWlpXI7hx07dlCXLl1IIpGQra2t0nL/REQjRoyo9D6GhIS8tH6qBAQEkLm5OWlqapK1tTV98cUXpFAoKsTbsGEDNWvWjDQ1Nally5a0c+dOpfPBwcEEgNLLbQuSn59Ps2fPJhMTE5JIJNS1a1e6ePFilfWpze0cXlRYWEiBgYHUvHlz0tTUJGNjY+rWrRvt27dPjBMUFESWlpYklUrJw8ODDh48SADo7NmzRKR6q4L58+eTg4MDyWQyMjAwoO7du4tpyu6Fn5+feK9Vbeewe/dupXzd3d1pypQp4mu5XE4SiaTK6yQq3a7E09OTpFIpGRkZ0bRp0yiv3L/vsvLKb61Rdm0vHuW37Zg+fTpZWVmRRCKhhg0bkru7O+3fv1+p7MDAQLKzsyOpVEoGBgbk4uJCmzZtopKSkpfW+0W8nQNj7xa3XVZXcdut26BiOweBVCwnXxe5urrS+fPnKz2XnJws/oL+SjIykLlxNRKk2chwsECBngxaOXlokpIJxxwdmEyaDbzmCpbs1Vy7dg2tWrVCXFwcXF1d33V1/rFycnIqXUGU1V+v/bn3DxEZGVlhajdjdQG3XVZXcdut2wRBuEBEFb4U81TP6jI3h0nAQnSOPoOCpPMofPYYmjoGMOw0CmrO7YC/9zxjjDHGGGOMsX8a7vjVhLY2qGMnNPR69RU1GWOMMcYYY+xt444fq/NatmyJ+jRlmTHGGGOMsdrGOwEzxhhjjDHGWD3HHT/GGGOMMcYYq+e448cYY4wxxhhj9dw76/gJguApCEKxIAg3yoWNFwSBKjl4NRXGGGOMMcYYe0XvZHEXQRDMAWwFcAxA8xdOFwNo/EJY1tuoF2OMMcYYY4zVR2+94ycIghqAnwH8AECKih0/EFHG264XY4wxxhhjjNVX72Kq5wIABGCZivPqgiDcEgQhXRCESEEQBrzFujHGGGOMMcZYvfNWR/wEQegO4F8A2hERCYLwYpQ/AUwEkAhAAuBjAAcFQfAlop9U5DkZwGQAMDMzQ2RkZKVlGxgYICcn57Wvobi4uFbyqYmQkBAEBwcjLS0N+fn5aNq0KcaPH4/p06ejknuo0u3bt9GmTRvxtbq6OszNzdGnTx8sWbIEenp6tVbnBg0aYO3atRgzZozKOPr6+lXm0bRpUyQlJdVanZYuXYqIiAhcuXIFubm5SE1NhZGR0Wvl+eDBAyxYsAARERF4+vQprKysMGXKFHz66adinIcPH2Lx4sU4efIksrKy0KZNGwQGBqJz585V5h0VFYWvv/4aSUlJ0NDQQP/+/bF06VIYGhqKcT755BNcvHgRGRkZkMlkcHZ2xoIFC+Di4lIhv6ra7vjx47F3714Ape3C0tISvXv3xvz589GgQYNXuTV12uHDh/HVV1/hxo0bsLCwwPTp0zFlypSXpktMTMRXX32FmJgYFBYWolmzZggODkbbtm0BAHv37sX27dtx6dIlZGZmYtu2bRgyZIhSHpGRkQgODsalS5dw7949BAYGYubMma90Hfn5+So/E+uS3NzcenEd7P3DbZfVVdx266e31vETBMEYpVM8J6iayklEZwGcLRd0VhAEIwByAJV2/IjoRwA/AoCrqyt169at0vKTk5Nfv2OjUOBZTAwkFy6AsrMhGBpCvWtXCO3bA9rar5d3FaysrLBw4ULY29tDIpHg1KlTmDZtGmQyGWbNmlXtfHR1dQEABw4cQMeOHVFUVISkpCR8+umnICL89FOlt/iVSaXSKu95enq6+Hd0dDSGDRuG+Ph4NGrUCEBpB6Q2O6MA8PHHH2PAgAFYsGABdHV1Xzv/IUOG4MmTJ9i3bx/Mzc3x+++/Y+bMmbCyssLQoUNRXFyM0aNHQ01NDXv27EHDhg3x3//+F0OGDMHFixdhb29fab7x8fEYOnQo5syZg61btyIzMxMzZ87EhAkTcOLECTGeh4cH/Pz80LhxY/z1119Yvnw5Bg8ejJSUFJiamirlmZOTo/J6NTU10aNHD2zfvh1FRUWIjY2Fr68vHj9+jD179rzWPapKcXExBEGAmto/Z4HhM2fOYOzYsZg3bx5Gjx6NU6dOYfr06TA2Nsb48eNVpouLi0Pfvn0xZcoUfPPNNzA0NMT169dhaWkp3vfi4mJ07doVU6ZMwfDhw6GtrV3hPSkpKUG7du0wYcIE/Otf/4JEInnldiqVStGuXbtXSvtPEhkZCVWf7Yz9k3HbZXUVt916iojeygGgG0qneBaVO0rKhY1WkW4KgILqlOHi4kKqXL16VeW5aklPp4I5cno8aRI9WriIMv+zjB4tXERPp02ngjlyovT018u/hoYMGUJDhgypUZrU1FQCQKdOnVIK9/PzIycnJ6WwY8eOkZubG0mlUrKwsKDx48fTo0ePxPNJSUnUq1cvMjAwIJlMRi1btqRt27YREZGVlRX9/b6Kx8tEREQQALp7926Fc0+ePKGJEyeSkZERSSQS6tixI4WHh4vnk5OTCQCFhISQh4cHSSQSatasGe3Zs6fSso4cOUIAKDMz86X1ehmpVEo//vijUpiDgwPNnTuXiIguX75MAOjixYtKcezt7Wny5Mkq8/X396e2bdsqhUVHRxMAio6OVpkuIyODAFBYWFiFc0+fPlWZbsSIEdS/f3+lsPnz55OWlhYVFBQQEdG9e/dozJgxZGRkRHp6etSlSxc6c+aMGL+goIAmTpxINjY2JJVKydbWlr788ksxPRGRXC4nR0dH2r59OzVv3pzU1dXp1q1blJCQQF5eXqSvr08ymYxatWpFoaGhYrq7d+/SRx99RPr6+qStrU2enp6UkJAgni97T8PDw8V26+joSMePH1d5zaoMHTqUunfvrhQ2Y8YMsre3rzJdx44dafz48dUqQ6FQEADavXt3lfHMzMxo+fLl1cqzMq/9ufeuFRQQpadTxPHjpX8zVsdERES86yow9kq47dZtAM5TJX2lt/kzexyANgCcyx3rAdz9++/DKtK1/zvOu6NQoHDVauSoqyPP3h4lRkYgmQwlRkbIb9kSORoaKFy1GlAo3nhViAixsbE4c+YMunfvLoZHRkZCEIQaD8vfunULYWFhcHNzE8PCw8MxePBgjBw5EpcuXcL+/fuRlpYGb2/vss44Ro0aBSMjI0RHR+Py5csICgoSpwTGxcVBXV0dq1evRnp6utLI3qvw8fFBZGQkQkNDER8fj/bt26Nv3764deuWUryAgABMnToViYmJ8Pb2xvDhw3H16tUaleXp6QldXd0qj7i4ODG+u7s7du/ejczMTBARjh49itTUVPTp0wdA6VQ7oHTkpTypVIqoqCiV9cjPz6+QRvvvUWVV6RQKBYKDg2FgYFArozza2tooLi5GcXExcnNz4eHhgeLiYhw7dgwXLlyAp6cnvLy8cPPmTQClo1mWlpYIDQ1FcnIyVqxYgXXr1mHFihVK+aampmLz5s345ZdfkJSUBFNTU3z88cdo3LgxYmJicPnyZSxfvlycClxSUoIBAwYgNTUVYWFhiImJgb6+Pnr06IHs7GylvAMCArBo0SIkJiaibdu2GD58uNL01g8++EB8b1Q5c+ZMhTh9+vTBn3/+iUePHlWa5v/+7/8QGxuLli1bonfv3jAxMYGrqyu2bNlSrXvNXvDsGQoOHsK9L7/GpTWboHjwCPcWLkXB4d+BvLx3XTvGGGOsbqqsN/i2DgCLANx44XU/AHYAHAEsROn2DtOrk9+bGvErOX2ank6bTg/XfE93V6ygh2u+r3A8nT6dSsqNftS27Oxs0tHRIU1NTVJXV6clS5YonT937hzZ29vTuXPnVOZRNuKnra1NOjo6JJFICAB5eXlRXl6eGM/Dw4PkcrlS2tu3byuNXOnr69PmzZtVlqWurl7l+RepGvFLSkoiAHTy5EkxrKSkhBwcHGjq1KlE9L8Rv8DAQKW07du3J19f3wplVTXid/fuXUpJSanyyM/PF+NnZ2fTwIEDCQBpaGiQVCql7du3i+cVCgU1btyYhgwZQpmZmVRQUEDr168nAKSvr6/yfhw6dIgEQaDNmzdTYWEhPXjwgPr160cAaObMmUpxV65cSTo6OiQIAllZWVF8fHyledZkxC8xMZGaNm1KHh4eREQUHBxMNjY2VFxcrJSuc+fOFdpKeUuXLqXWrVuLr+VyOamrq9P9+/fFsJKSEpJIJBQSElJpHmX3IiUlRQx79uwZGRkZ0bJly4jof+/p4cOHxThpaWkEgCIjI5Wus7I2Uaa4uJgA0NatW5XCz58/TwDo0qVLlaaLjIwkACSTyWjlypV08eJF+u6770hTU1McCS+PR/yq8Ndf9ChwGf32zX9p7b7zFHTsT9r5Wxj9sDeODi79kR5/s5woJ+dd15KxauFRE1ZXcdut26BixO+d7ONXBX2UbvNgDkAB4BqA4UT05h4yqobiU6dQYGJSZZwCY2MUR0VBo9zIWW3S09NDQkIC8vLyEB0djc8//xwWFhbiIiIdO3bEtWvXqpXX5s2b4eLiguLiYqSkpMDf3x/jx4/Hzp07AZSO2MXExGDt2rUV0qakpMDZ2RkBAQHw9fXFli1b0K1bNwwaNAjt27evvQv+25UrV6CmpoYuXbqIYYIgoGvXrrhy5YpS3BcXS3Fzc8OFCxdqVF7jxi9uIVm1efPm4f79+zh27BjMzMxw/PhxTJkyBWZmZujZsyekUin279+PSZMmwcTEBOrq6nBzc8PIkSMRFhamMt/+/ftj5cqVmD17Nnx9faGhoQG5XI7o6OgKz8NNnDgRgwYNQkZGBoKDg+Ht7Y3o6GjxWcnqCgsLg66uLoqKilBYWIhevXph/fr1AErbxJ07dyosyPP8+XOlctatW4fNmzfj9u3byMvLQ1FREbS0tJTSNGnSRCmNIAgICAiAj48PfvzxR3Tr1g2DBw8WF0S5cuUKLCwsYGdnJ6aRyWRwdXWt0AacnZ3Fvy0sLACULsBTJjQ0tEb3pLpKSkoAAN7e3vDz8xPrcunSJXz//ffw8fF5I+XWR09DdiNS1hh3WrsqhT/X1UeKqwcKL51Dz917oTth3DuqIWOMMVY3vdMVFYhoERHZlXvtR0Q2RKRNRA2JyO1dd/oAgLKzQS9ZvIW0ZaC//npjdVBTU4OdnR2cnJzwr3/9C3PmzMEXX3zxSnlZWlrCzs4O9vb2GDBgABYvXoxdu3bhxo0bAEq/xMrlciQkJCgdKSkp6Nu3LwBgwYIFuH79OoYPH46kpCR88MEHmD9/fq1d77tSk6meV69exbp16/DTTz+hZ8+ecHJygr+/P4YMGYJly/63W4mLiwvi4+ORnZ2N+/fvIyoqCs+ePUOzZs2qrMu///1vPHnyBHfv3kVWVhZmzZqF7OzsCukMDQ1hZ2eHLl264OeffwYRYcOGDTW+9g8//BAJCQm4du0aFAoFjhw5AisrKwClbcLZ2blCm0hOThZ/INi+fTv8/Pzg4+ODI0eO4OLFi5DL5SgoKFAqR0dHp0LZgYGBSE5Ohre3Ny5evIgOHTrgq6++qvE1lO9klq14W9Ypqw41NTWYmpoiI0N5/amyzqOqznRZuKOjo1K4o6Mjbt++Xe3y33uZmUhPuYO7rVRPVb7j0B4ZV1KArKy3WDHGGGOs7vunjfj9IwmGhhAUCpBMpjqOIg+CgcFbq1NJSYn4/NjrUldXB1D6jBgAcSSl/AhLZWxtbTFt2jRMmzYN//nPf7B8+XIEBgYCKP0CXlxc/Np1c3R0RElJCU6fPg1PT08ApdOTT506VWG1qZiYGDEOULpSaE2fddu2bdtL72uTJk0AAM+ePQOACiNw6urq4rOQ5Rn83T4ePHiAkydPVqujLAiC2KnYtGkTNDQ0MGjQoCrTENErtQ2ZTKbyPXd1dcXevXvRsGFDNGzYsNI4UVFR6NSpk9LWA6mpqdUu387ODjNmzMCMGTOwaNEirF+/HgsWLICjoyPu37+PGzduiPXLy8vD+fPnIZfLa3CF1ePu7o6jR49izpw5YlhYWBjs7e1hbGxcaZoWLVrAzMyswqj7n3/+CWtr61qvY711/TquGzcF/f2ZVJkSDU3cMGoCu5QUoFOnt1g5xhhjrG7jjl81qHftCq0dIcivYs83rUePoD569Bspf+HChejatStsbW1RWFiIqKgoLFu2DBMmTBDjxMbGYty4cdi2bRs6duxYZX5ZWVnIyMgQp3ouWbIE9vb2aNWqFQBgyZIl6NWrF/z8/DBu3Djo6ekhJSUFu3fvxtq1a1FcXAy5XI5hw4bBxsYG2dnZCAsLg4ODg1iGjY0NIiIi0LdvX2hpaan8wvwyjo6OGDhwICZPnoz169fD0tISa9aswc2bN3Hw4EGluMHBwbCzs4OzszM2bdqEhIQEbN26VTx/+/ZtPHnyRFwUJikpCYaGhrC2thb3xqvJVE8nJyfY2Nhg6tSpWL58uTjVMzQ0FKtWrRLj7dy5E6amprC2tkZycjL8/PxgZ2eH2bNni3GCgoKwdetWJCYmAijt2AcFBaF3797Q0NDA4cOH8cUXX2DRokVo2rQpgNItH8qWWzY2NkZGRga+//57ZGRkYNSoUTW801X75JNPsGbNGgwYMABfffUVmjVrhoyMDJw4cQLt2rVD//79YW9vj9DQUBw+fBj29vbYv38/Dh069NK8s7KysGjRIgwdOhQ2NjZ4/Pgxjh8/Lranvn37wsnJCaNGjcKaNWugq6uLL7/8EoIgYPLkyTW6jpEjR0JPTw8bN25UGcff3x8eHh5YvHgxRo4cidOnT2PDhg1Ko6inT5+Gr68vdu7cibZt20JNTQ1yuRxyuRxubm7w8vLCH3/8gS1btmDz5s1iusePH+Pu3bviKGhaWhoSEhJgbGwstr2cnBxxwZyioiLcv38fCQkJ0NfXh62tbY2ut84pLMRzDc2XRnuurgUUFr6FCjHGGGP1SGUP/tXV441t55CXV7qVw7wvKl3c5fHn80q3dCi3QEptmj17NjVr1oykUikZGhpS+/btae3atVRUVCTGKVscpaqHccsWdyk71NTUyMLCgkaPHk2pqalKcaOiosjLy4t0dXXF7RpmzZpFhYWFpFAoaNSoUWRtbU0SiYRMTExo+PDhdOfOHTH9kSNHqGXLlqSpqVmr2zloaWmp3M5hx44d1KVLF5JIJGRra0u7du1SymfEiBEVtpnA39tAvKrr16/TsGHDyMzMjLS1talVq1a0atUqKikpEeMEBQVRkyZNSFNTkxo1akTTp0+nrKwspXzkcjlJJBLxdXFxMXXr1o0MDAxIIpFQ27ZtKyyWk5ycTD179iQTExPS1NQkS0tLGjp0KMXGxlZa15pu5/Cihw8fkq+vL5mbm4vlDRs2TFzwJD8/nyZMmECGhoakr69PPj4+tHLlSqXrKtvOobycnBwaMWIEWVlZkZaWFpmamtLo0aOVFoC5e/cuDRs2TNzOoXv37kpbZFS2YE9hYWGF97dTp07Uu3fvKq+TiGjfvn3UunVr0tTUJGtra1qzZo3S+bLyzp49qxS+YsUKsra2JqlUSm3atKmwSExwcHClbXDKlCkV8n7xqE69X1TnFndJSKA/Fn1HQcf+VDp2/ham9Pr0l0FEly+/69oy9lK8QAarq7jt1m1QsbiLQJVMSaurXF1d6fz585WeS05OFke0XklGRumWDtlPQI0agbRlEBR50Hr0CFIdXWj+ezZgbv7q+bNXdu3aNbRq1QpxcXFwdXV9eYL3VFUbuLP66bU/9962ggL835dL8VunAXiu+7+FhCzzU3FPagMAkD59gsHxR2GxeB6gwZNW2D8bb4LN6ipuu3WbIAgXiKjCl+J3urhLnWJuDs1FCyH5+GMYSKUwyH4CA6kUuqNHQ3PRQu70McbY69LSgnlfT3Q4dxwa+RX3RdVUPEPH2JMw7deDO32MMcZYDfH/OWtCWxslnTpBo0ePd10TxhirlzQ+7Iq2z/PR4PgeXDFrhkemjaGmU4BmyWfg+DAVVn27QcOt88szYowxxpgS7vixOq9ly5aVrqLJGKuDBAFavXrBzsUFTc+dQ+7NFFyUGqGrUyNodRwMqFhVljHGGGNV444fY4yxfx4jI2j164eGANQjI6HFz5owxhhjr4Wf8WOMMcYYY4yxeu696vjxdEDG2PuCP+8YY4wxVt570/HT1NSEQlFxlTjGGKuPFAoFNDVfvhk6Y4wxxt4P703Hz9TUFPfu3UNeXh7/Es4Yq7eICHl5ebh37x5MTU3fdXUYY4wx9g/x3izuoq9fuhnw/fv3UVhY+Mr55OfnQyqV1la1GHtruO2+PzQ1NWFmZiZ+7jHGGGOMvTcdP6C08/e6X4QiIyPRrl27WqoRY28Pt13GGGOMsffXezPVkzHGGGOMMcbeV9zxY4wxxhhjjLF6jjt+jDHGGGOMMVbPccePMcYYY4wxxuo57vgxxhhjjDHGWD0n1Kc97QRByARw+w0XYwzg0Rsug7E3gdsuq6u47bK6itsuq6u47dZtVkRk8mJgver4vQ2CIJwnItd3XQ/GaorbLquruO2yuorbLquruO3WTzzVkzHGGGOMMcbqOe74McYYY4wxxlg9xx2/mvvxXVeAsVfEbZfVVdx2WV3FbZfVVdx26yF+xo8xxhhjjDHG6jke8WOMMcYYY4yxeo4jqIfUAAAOS0lEQVQ7fowxxhhjjDFWz72XHT9BENQEQfhSEIQbgiAoBEG4IwjCGkEQdMrF0RAEYY4gCH8KgpAvCEKKIAjTq5l/G0EQfhMEIVsQhDxBEC4JgtDxzV0Re1+8qbYrCIK1IAik4vjhzV8Zq+/e5OeuIAgmgiBsEgTh/t95JwuC8P/e7BWx98UbbrtWgiCECIKQ8ff3hZOCILR9s1fE3ieCIOgIgvAfQRBu/d02Lwv/v717j9eqqvM4/vlydZhXSkneFRQdTFDUBjMl9YU3HCovKA5mCCZo+gpDp7JRX2o6DaNgmXbRvA3lZUSTRgtSGxMvaQMqio5NSTHIJUEkQ0QEf/PHWo9uducAh/NsD5zn+3699uu411577d86Z/H4rGddHunEUp5PSHoiX18o6V8lddyAskfmNv+2pJckfa66mlg9dGrrANrI+cA/AaOAmUAf4BagK3BmznMZMCYfs4BPAjdIWhURP2yu4PyC/Shwcy5jKbAH8HolNbFGU1XbnQdsX0obCEwG7qxnBaxhVfa6C9wK7AKcBCwAjgS+J2lJRNxR/6pYg6mk7UrqBjwIzAH+AXgrP+e/JO0VEX+qrEbWSG4ADiS11Vpbu0PSGxHxgKSdSe3wHmA06T3rzYCAC5orVNJxwE2kNjsV+DQwSdLSiJhaYX2sFRpycxdJU4A1ETG0kDYRGBQR++XzV4DrImJ8Ic81wLER0WsdZT8CLIiI4VXFb42ryrbbxLNuA/aNiL71it8aV8Wvu8uAiyPi2kLaTGB6RIyre2WsoVTVdiUdQXrDvX1ELMppHYFXc1mXVFQlaxCStgD+Anw+Iu4spP8U6B4Rh0r6JjAC2CUi3s3XzwGuBLaJiDebKfsJ4I8RcUohbTLw0Yg4rKo6Wes05FRP4DHgYEn7AEjajfQJyM8KebYAVpbuewvoKalnU4VK6gEcAsyW9HNJiyXNlDSm7jWwRlVJ2y3LbXkocH2rIzZLqmy7jwFDJW2rZBBpVMafOls9VNV2t8g/37svItYAq0jvJcxaqzPQkabb5oGSOgMHAw/UOn3ZNKAbsF9ThUrqAgzI+Yqm5XLXO03U2kajdvwmAtcBT0t6B3iZND3z4kKeqcBYpfV6kvQJ4PR8bYdmyu2df14IPEKabnQT8B1Jo+tcB2tMVbXdspHAu8CkukRtVm3bHQ4sARaR3jRPBcZGxAN1roM1pqra7pPAMmCipC0ldZV0EbDdOu4x22AR8RfgceBCpbX8HSQdAxwLdAF6kJZ5LCrdWjsvLwGp6UFaLtbUfV2Bj9QhfKtAo3b8TgTOJs3X35+0LuQY4IpCnnOBGcCzwDuktU435WvFT0WKar/Pn0XEv0XEsxHxPeBGwBsNWD1U1XbfI0mkdSp3RcSyukVuja7KtnspsHsu7+PAV4BrJQ2pX/jWwCppuxGxBDiBtJ56GfAmaS3Wz5u7x2wjnEpqX3NIH4xNIL0vBbezhtOom7tMBK6JiB/l8+cl/Q1ws6TLI2JlRCwFhuXh7G1IGwaclfPPaabchfnnC6X0F4DP1y98a2BVtd2iQaTF3SPqHLs1tkrarqTewHnAgRHxVE5+Lm+09XXWno5ntjEqe92NiIeBPpI+DHSIiNck/YY0qmjWahExFzgybybUPSIWSLoSeANYTHrvul3ptm3zz4U0bQmwupn73iZtbGiboEYd8ftb/vpTjjWkHYxUTIyIVRHxSp77PJy0WcDiZsqdS9odcc9Seh/gj60N2ozq2m7RmcBzEfFkPQI2y6pqu93yz+bKNmutyl93I+L13OnrQxq1vqc+oZslEbEid/q6kEaxp+R2+jipY1jsEwwGVgDPNFPWKuC/gaNLlwYDT+a1qrYpioiGO0jTL/4EHA/0IjXcOcB9hTwDSNM5epO2Zb6btDNS/0KeA4CXgAMKaV8kfQpyTr53BOkfz6i2rrePzf+osu3m9G1JU0HObuu6+mhfR1VtlzRz5bfAU6RNCnYlra1aCZzf1vX2sfkfFb9nGJnb7W6kDbXmAQ8DHdu63j7ax0Hab2JIbmOHkvagWAjsmK/vTBr9uwnoC3wWeA0YXyhjx9x2jy+kHZff755LGuA4L58f09Z19tH80ahTPceShqEnkhZQvwrcD1xUyNMVuIT0Ir4KmA4cFBHPF/J0IzX22ifORMT3JXUi/QOYQJquMTYibqmsNtZIKmu72ah8z4+rCN4aWiVtNyJW580Kvkl6s92dNPviYuBbFdbHGkeVr7u7k9puD9Kb8duBy8IjJlY/WwLjSd91uhz4BTAiIuYDRMQ8SUcBV5O+p3IZ6bv/iu27M6ntblVLiIgpks4A/hm4CvgDMDL8HX6btIb8Hj8zMzMzM7NG0qhr/MzMzMzMzBqGO35mZmZmZmbtnDt+ZmZmZmZm7Zw7fmZmZmZmZu2cO35mZmZmZmbtnDt+ZmZmZmZm7Zw7fmZmDUrSFZJeaus42pKkIySFpO3aOhYzM7MqueNnZtZOSbo1d2rKxz/mLOOBgR9QLIflZ+/0QTyvHiR1aub3Vzx+X+dnXi7pcUnLc/ndW3j/o5LWSDq0nnGZmdnmr1NbB2BmZpV6FBhWSlsGEBHLgeUfeESbiYhYLWn7QtKngLuAfYDFOW11nR/bBfgJ8ABwaUtulLQXsB/wHWAM8EidY2sxSV0iYlVbx2FmZh7xMzNr71ZFxKLSsRL+eqpn7VzSCZJ+m0edHpbUu1igpAGSHszXX5V0t6SdmwtA0u7Aw/l0Xh7Jeihf+7GkaaX8IyWtLpzXLS5JX5b0iqQVkqYC6xyBLP7egNdz8uJC+pJc7ocl3SJpiaSVkp4sjrpJ6pfrPUzS9Jznd5KOLT3vaxExEZi5rriaMQa4B7gOOEHS1uUMkoZI+rWktyS9LumXxVFYSadJmpXjWyLpPknd8rUZkr5dKm+8pNmF87slTZH0VUn/B7yZ0z8j6bH8zNpz9y2VtZWk70qaL+ltSS9LGld49tWl/J0kLajlMTOzdXPHz8zMinYCRgPDSdNAuwM31i5K2hv4FWkk8e+BIwABD0rq0kyZfwCG5v/eH9geOOmDjkvSUOCqfOxLGlm7soVxNOf2HNfJpDo+B0yT1KuUbwKpY9YfuA+4W1Kf1j5c0hbACODWiHgZmAGcVsrzWeA/SSOBB+R4JwOd8/UvATcAt5F+P4fnvB1bGM7h+f4hpL8FQDfg6vzcQ4CFwFRJH8rP7kga5Tyc1IH9GHAGeXQauB4YIalr4TlDgI8A/97C+MzMGpKnepqZtW+HSSpO55wfEevqaHQFTo2I1wAkTQAmSeocEe8AXwPujYhv1G6QdCrpDfpRwP3lAiNijaSl+XRxHj1rqXrE9RXg9oi4Jmf5X0l9gXM3Ip73SOoPDAYOjYjpOe0sUgfnPGBsIft3I+Ku/N/nSToc+DLwxdbEQOpI/5nU+YXUGTqf1NmquQyYHBEXFNJeyPF2AC4BvhURxc7wrI2IZQUwKiLeriVExH8UM0gaBbwBDAJ+CnwaGAD0i4gXc7Y5hVvuACYCxwN35rQzgHsiYilmZrZeHvEzM2vfniKNvtSOo9eTf16tc5UtIP2/4qP5fABwUp5OuTx3KheTRo32kNSxeE3SfXWqR6viynn2Ap4olftYHWLrC6wplh0R7+ay+5by/rp0/kQTeTbGGGBSREQ+vwvoWZtumkfU+pNG1ZrSC9h6Hddb4rlipy8//+8k3ZGnb75BmjbbFeiZs3wceKXQ6VtLXo96G2nUF0k7AscAP6xDvGZmDcEjfmZm7dtbEdGSnSfLG3HUOhIdCj9vJU2XLFuSR/eKa7dWrOd575KmZBZ1rndc64lhs6a0qctA4CBJFxYudaS+m7xs6N/qzVJ8An4B/A44k9RpXwU8TdrMZkP9AHgmr+0cDrwcEb9qwf1mZg3NI35mZtYSM4B9IuL3TRy13UKLaQvyfbWOW3m92KvADqW0/auIC3gROKh038Eb8ayyF0j1eq/sPHVyIDC7lPfA0vknc1ytMYZU//6sPbp7OjBU0tYRsYY0bfOoZsqYC7y2juuw8X+rXUgjipdHxEN5VK8D8KFCnpnATpI+1lwhETEL+A2pvl+gsMbTzMzWzx0/MzNriX8B9pY0Ke+iuZukQZKuldRzHffNJY3SDZG0jaQtc/pDQD9JZ0nqLelM3t8Ipt5xTQROkfQlSXtI+gJwykY8ay25QzIVuDE/cy/S6FRP1l5jB3COpBPz1MergH5Abc0hknrlEdNdc9LekvaVtFVTzy5s6nJ7RMwuHsCPSKNvtU1evgEMyztx9pO0p6TRknrljuEVwLi8I+eeOc+42gYspL/VEEnH5t/fpaSvj1ifRaT1fGdJ2l3SQNIaxOJ00PtJI4D3Ku08uqukQySdVirretKayB1II7xmZraB3PEzM7MNljsUB5N21XyQNNp1A2nK3p/Xcd984ELgItKOjj/J6dNIm4pcTBqR+hSpA1L3uCJiMmkTmK+Tdt08GbigqfI2wueAx0m7ZD5N+q6/wRExt5Tvq8A44HnSRiXDIuJ/CtcnAM+QvosPYHo+P7KZ555EqvPk8oWIWE36PY/O5/cCx5F2zpxBWm94MvBOvv5t4GxSR3IW6Ss4DiOtXwT4PjCJNNL2FGnEbr1r7PJ6v2GkUcjZpM7bFby/Yye543kkaVrqzcBL+Wf5C+zvBFYCUyJiMWZmtsH0/jpwMzMzq4KkfqTO3n4R8Wxbx7O5krQDafR4cET8sq3jMTPbnHhzFzMzM9ukSeoM9ADGAy+602dm1nKe6mlmZmabuqOB+aTNcU5v41jMzDZLnuppZmZmZmbWznnEz8zMzMzMrJ1zx8/MzMzMzKydc8fPzMzMzMysnXPHz8zMzMzMrJ1zx8/MzMzMzKydc8fPzMzMzMysnft/gih5UbAlb74AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_sorted = df_plain20_ft60.sort_values(by=['top1'], ascending=False)\n",
+ "df_sorted = df_sorted[:int(len(df_sorted.index) * 0.5)] # Top 30% models\n",
+ "create_fig(\"Discovered Networks - Zoom View:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "plot_networks_by_experiment(df_sorted, edgecolors='red')\n",
+ "\n",
+ "from scipy.stats.stats import pearsonr\n",
+ "print(pearsonr(df_sorted.top1, df_sorted.search_top1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Clipped PPO\n",
+ "What happens if we use a different agent, such as Clipped PPO?\n",
+ "Results are interesting and encouraging as there is learning. However, this is less sample-efficient compared to DDPG, and therefore takes longer.\n",
+ "There might be some room to tune the PPO hyper-params to generate better results.\n",
+ "\n",
+ " time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-ppo-coach amc.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=ClippedPPO-continuous --amc-cfg=auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=coach -j=1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5ae394da6ea9414eb33ea94dcec60344",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), IntSlider(value=0, descr…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR, \"plain20-ppo-coach\", \"2019.07.28-012356\")\n",
+ "df_list = load_experiment_instances(fpath)\n",
+ "df_len = min([len(df) for df in df_list])\n",
+ "\n",
+ "plot_experiment_comparison(df_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-ppo-coach/2019.07.28-012356 --arch=plain20_cifar --lr=0.1 --vs=0 -p=50 --compress=plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --epochs=60 --output-csv=ft_60epoch_results.csv --processes=16"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_plain20_ft60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
+ "shorten_dir_name(df_plain20_ft60)\n",
+ "\n",
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "plot_networks_by_experiment(df_plain20_ft60, edgecolors=None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using a different reward function\n",
+ "\n",
+ " time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-ddpg-private-punish compress_classifier.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc-protocol=punish-agent --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=DDPG --amc-cfg=../automated_deep_compression/auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=private -j=1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e4e681410db3417e95b3aa2f0f697d7f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), IntSlider(value=0, descr…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR, \"plain20-ddpg-private-punish\", \"2019.07.29-171102\")\n",
+ "df_list = load_experiment_instances(fpath)\n",
+ "df_len = min([len(df) for df in df_list])\n",
+ "\n",
+ "plot_experiment_comparison(df_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-ddpg-private-punish/2019.07.29-171102 --arch=plain20_cifar --lr=0.1 --vs=0 -p=50 --compress=plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --epochs=60 --output-csv=ft_60epoch_results.csv --processes=16"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_plain20_ft60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
+ "shorten_dir_name(df_plain20_ft60)\n",
+ "#df_plain20_ft60 = df_plain20_ft60[df_plain20_ft60[df_plain20_ft60.macs] >= 50.5] \n",
+ "\n",
+ "create_fig(\"Discovered Networks - Zoom View:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "plot_networks_by_experiment(df_plain20_ft60, edgecolors=None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Increase the number of images we use for scoring\n",
+ "\n",
+ "Let's go back to DDPG, and try to assess what happens when we increase the validation dataset.\n",
+ "We use the validation dataset both for reconstruction of feature-maps and for computing the reward. Our original configuration is:\n",
+ "\n",
+ " Dataset sizes:\n",
+ " training=22500\n",
+ " validation=2500\n",
+ " test=750\n",
+ "\n",
+ "Since we don't fine-tune in this setup, the size of the training DS is irrelevant. The same is true for the test DS, since it's not used.\n",
+ "\n",
+ " Dataset sizes:\n",
+ " training=450\n",
+ " validation=5000\n",
+ " test=750\n",
+ "\n",
+ " time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-ddpg-private amc.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=${CHECKPOINTS_PATH}/checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=DDPG --amc-cfg=auto_compression_channels.yaml --evs=1.0 --etrs=0.01 --amc-rllib=private -j=1\n",
+ " \n",
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-ddpg-private/2019.08.01-181040 --arch=plain20_cifar --lr=0.1 --vs=0 -p=50 --compress=plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --epochs=60 --output-csv=ft_60epoch_results.csv --processes=16"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR,\n",
+ " \"plain20-ddpg-private\",\n",
+ " \"2019.08.01-181040\")\n",
+ "df_plain20_ft60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
+ "shorten_dir_name(df_plain20_ft60)\n",
+ "#df_plain20_ft60 = df_plain20_ft60[df_plain20_ft60[df_plain20_ft60.macs] >= 50.5] \n",
+ "\n",
+ "create_fig(\"Discovered Networks - Zoom View:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "plot_networks_by_experiment(df_plain20_ft60, edgecolors=None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now repeating the original experiment, with checkpoint savings so we can fine-tune the best ones\n",
+ "\n",
+ " time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-ddpg-private amc.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=${CHECKPOINTS_PATH}/checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=DDPG --amc-cfg=auto_compression_channels.yaml --evs=0.5 --etrs=0.01 --amc-rllib=private -j=1 --amc-save-chkpts\n",
+ " \n",
+ " time python ../../classifier_compression/parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-ddpg-private/2019.08.03-000628 --arch=plain20_cifar --lr=0.1 --vs=0 -p=50 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --epochs=60 --output-csv=ft_60epoch_results.csv --processes=16 --top-performing-chkpts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR,\n",
+ " \"plain20-ddpg-private\",\n",
+ " \"2019.08.03-000628\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_plain20_ft60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
+ "shorten_dir_name(df_plain20_ft60)\n",
+ "\n",
+ "create_fig(\"Discovered Networks - Zoom View:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "plot_networks_by_experiment(df_plain20_ft60, edgecolors=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(89.86, 89.65263157894738, 0.14771752718027756)"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "_mean = df_plain20_ft60.top1.mean()\n",
+ "_std = df_plain20_ft60.top1.std()\n",
+ "_max = mean = df_plain20_ft60.top1.max()\n",
+ "_max, _mean, _std"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a3c0433eba7a490b8b762a81fd84e4cd",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), IntSlider(value=0, descr…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_list = load_experiment_instances(fpath)\n",
+ "plot_experiment_comparison(df_list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.7"
+ },
+ "pycharm": {
+ "stem_cell": {
+ "cell_type": "raw",
+ "source": [],
+ "metadata": {
+ "collapsed": false
+ }
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/auto_compression/amc/jupyter/amc_random.ipynb b/examples/auto_compression/amc/jupyter/amc_random.ipynb
new file mode 100644
index 0000000..0de236e
--- /dev/null
+++ b/examples/auto_compression/amc/jupyter/amc_random.ipynb
@@ -0,0 +1,453 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When searching for networks using AMC, we need to be able to rank discovered-networks by their accuracy. In reality, we use a multi-objective (such as accuracy and latency) for produce a reward signal to the agent, but we always need to know how well the discovered networks perform their classification task. We don't need to know the \"true Top1\" accuracy of the models (the accuracy of the model trained to convergence): we just need to be able to rank them, in a stable manner. For example, imagine we've discovered two networks in our search: netA and netB. NetA gives us a low predicted-accuracy signal (\"search-Top1\"), and NetB gives us a high predicted-accuracy. If NetA's true-Top1 is smaller than NetB's true-Top1, and this property holds for most pairs of discovered networks, then the ranking is considered stable.\n",
+ "\n",
+ "#### Question: \n",
+ "* Does FM-reconstruction improve the stability of model ranking?\n",
+ "\n",
+ "#### Method:\n",
+ "* Use an agent with a random policy, so the RL agent does not play a role. \n",
+ "* Compare the ranking stability when using FM-reconstruction vs. when using fine-tuning.\n",
+ "\n",
+ "#### Baseline:\n",
+ "* L1 ranking with Random agent\n",
+ "* 1 FT epoch\n",
+ "\n",
+ "#### Test:\n",
+ "* Reconstruction with Random agent\n",
+ "* 1 FT epoch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd \n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib \n",
+ "import csv\n",
+ "from matplotlib.ticker import FuncFormatter\n",
+ "import ipywidgets as widgets\n",
+ "from ipywidgets import interactive, interact, Layout\n",
+ "import matplotlib.pylab as pylab\n",
+ "import matplotlib.animation as animation\n",
+ "from matplotlib import animation, rc\n",
+ "from scipy.stats.stats import pearsonr\n",
+ "from auto_compression_jupyter import *\n",
+ "\n",
+ "\n",
+ "EXPERIMENTS_DIR = os.path.join(\"/experiments\", \"amc\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Test**\n",
+ "\n",
+ " time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-random-reconstruction_nondeterministic amc.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=Random-policy --amc-cfg=../automated_deep_compression/auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=random -j=1\n",
+ "\n",
+ "==> experiments/plain20-random-reconstruction/2019.07.22-120953/\n",
+ "\n",
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-reconstruction_nondeterministic/2019.07.23-124600 --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../automated_deep_compression/fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --ft-epochs=1 --output-csv=ft_1epoch_results.csv\n",
+ " \n",
+ "**Baseline**\n",
+ "\n",
+ " time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/plain20-random-l1_rank amc.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=l1-rank --amc-agent-algo=Random-policy --amc-cfg=../automated_deep_compression/auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=random -j=1\n",
+ "\n",
+ "==> classifier_compression/experiments/plain20-random-l1_rank/2019.07.21-004045\n",
+ "\n",
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-l1_rank/2019.07.21-004045/ --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../automated_deep_compression/fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --ft-epochs=1 --output-csv=ft_1epoch_results.csv\n",
+ "\n",
+ "==> classifier_compression/experiments/plain20-random-l1_rank/2019.07.21-004045/ft_1epoch_results.csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "#### NOTE\n",
+ "\n",
+ "* We use ```multi-run.py``` to execute the experiment 4 times, and write the output of each experiment to a separate subdirectory of ```experiments/plain20-random-l1_rank/2019.07.21-004045```.\n",
+ "* Then we use `parallel-finetune.py` to traverse these subdirectories.\n",
+ "* In each experiement subdirectory there are checkpoints for each of the best discovered networks. Each time we find a network that performs better than the best network discovered so far (as measured by our multi-objective), we save the checkpoint of that netowrk.\n",
+ "* Script `parallel-finetune.py` will run a fine-tuning (re-training) session of each of the network-checkpoint files and write the accuracy and other metadata of each fine-tuned network to a CSV file.\n",
+ "* So if we execute 4 experiments using `multi-run.py`, then the CSV file should have the results for checkpoints of each of these 4 experiments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_baseline = pd.read_csv(os.path.join(EXPERIMENTS_DIR,\n",
+ " \"plain20-random-l1_rank\",\n",
+ " \"2019.07.21-004045\",\n",
+ " \"ft_1epoch_results.csv\"))\n",
+ "df_thesis = pd.read_csv(os.path.join(EXPERIMENTS_DIR,\n",
+ " \"plain20-random-reconstruction_nondeterministic\",\n",
+ " \"2019.07.23-124600\",\n",
+ " \"ft_1epoch_results.csv\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Answer:\n",
+ "\n",
+ "* When using L1-ranking (with no FT during the RL search process) with a Random agent, there is no corralation between the search Top1 accuracy used as reward, and the Top1 after 1 epoch of FT.\n",
+ "* When using FM-reconstruction, search Top1 scores below ~15% show no corralation to the Top1 after 1 epoch of FT, but higher search Top1 scores show much better corralation (to be measured)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x7fa147eb98d0>"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(15,7))\n",
+ "plt.scatter(df_baseline.top1, df_baseline.search_top1, s=df_baseline.macs)\n",
+ "plt.scatter(df_thesis.top1, df_thesis.search_top1, s=df_thesis.macs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "OK, so clearly performing feature-map reconstruction helps create a much richer reward signal.<br>\n",
+ "But how accurate is this signal?<br>\n",
+ "We take some of the networks from the feature-map reconstruction experiment and fine-tune them for a longer period.\n",
+ "\n",
+ "\n",
+ "#### Question:\n",
+ "* What happens if we fine-tune the discovered networks for a longer time?<br>\n",
+ " NOTE: we fine-tune _AFTER_ the RL search is done, so this FT process does not help the agent. And, in any case, we are using a random agent.\n",
+ " \n",
+ "<code>\n",
+ "time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-reconstruction_nondeterministic/2019.07.23-124600 --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --ft-epochs=20 --output-csv=ft_20epoch_results.csv\n",
+ "</code>\n",
+ "\n",
+ "#### Answer:\n",
+ "* The Pearson corralation **decreases** the more we fine-tune the solution. This is not good...\n",
+ "* But something interesting happens: the discovered networks that have search-Top1 below ~15% still have low ranking stability (i.e. they do not corrolate well with the \"true Top1\")."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best network: 82.54\n",
+ "Pearson: 0.753\n",
+ "Best network: 84.65\n",
+ "Pearson: 0.698\n",
+ "Best network: 85.97\n",
+ "Pearson: 0.656\n",
+ "Best network: 87.60\n",
+ "Pearson: 0.594\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR, \n",
+ " \"plain20-random-reconstruction_nondeterministic\",\n",
+ " \"2019.07.23-124600\")\n",
+ "df_thesis3 = pd.read_csv(os.path.join(fpath, \"ft_3epoch_results.csv\"))\n",
+ "df_thesis6 = pd.read_csv(os.path.join(fpath, \"ft_6epoch_results.csv\"))\n",
+ "df_thesis20 = pd.read_csv(os.path.join(fpath, \"ft_20epoch_results.csv\"))\n",
+ "\n",
+ "def plot_networks2(df):\n",
+ " size = df.macs / max(df.macs) * 100\n",
+ " plt.scatter(df.top1, df.search_top1, s=size)\n",
+ " print(\"Best network: %.2f\" % max(df.top1))\n",
+ " df_sorted = df.sort_values(by=['top1'], inplace=False, ascending=False)\n",
+ "\n",
+ "\n",
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "for df in (df_thesis, df_thesis3, df_thesis6, df_thesis20):\n",
+ " plot_networks2(df)\n",
+ " print(\"Pearson: %.3f\" % pearsonr(df.top1, df.search_top1)[0])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Question (Part 2):\n",
+ "What happens when we look at the discovered networks that have a Search Top1 > 20%? <br>\n",
+ "I.e. if we remove the noisy low-grade networks, can we improve the corrallation?\n",
+ "\n",
+ "#### Answer (Part 2):\n",
+ "Yes, the corralation increases and we have some evidence of a quality reward signal. \n",
+ "\n",
+ "@todo: compare to the networks trained to **convergence**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pearson: 0.887\n",
+ "Pearson: 0.905\n",
+ "Pearson: 0.889\n",
+ "Pearson: 0.901\n"
+ ]
+ }
+ ],
+ "source": [
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\nsearch_top1 > 20%\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "for df in (df_thesis, df_thesis3, df_thesis6, df_thesis20):\n",
+ " df = df[df['search_top1'] > 20]\n",
+ " plot_networks2(df)\n",
+ " print(\"Pearson: %.3f\" % pearsonr(df.top1, df.search_top1)[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Question:\n",
+ "\n",
+ "* What happens if we fine-tune the networks we discovered using l1-ranking (without FM reconstruction)?\n",
+ "* In other words, how many fine-tuning epochs do we need to perform during the RL search to get a cleaner, less noisy (i.e. more stable) search-Top1 (used in the multi-objective reward signal)?\n",
+ "\n",
+ "\n",
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-l1_rank/2019.07.21-004045/ --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../automated_deep_compression/fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --ft-epochs=3 --output-csv=ft_3epoch_results.csv\n",
+ "\n",
+ " time python para,,e,-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-l1_rank/2019.07.21-004045/ --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../automated_deep_compression/fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --ft-epochs=6 --output-csv=ft_6epoch_results.csv\n",
+ "\n",
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/plain20-random-l1_rank/2019.07.21-004045/ --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../automated_deep_compression/fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --ft-epochs=20 --output-csv=ft_20epoch_results.csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Answer:\n",
+ "\n",
+ "* Fine-tuning longer does not help increase the quality of the search-Top1 accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best network: 72.17\n",
+ "Pearson: 0.129\n",
+ "Best network: 81.88\n",
+ "Pearson: 0.137\n",
+ "Best network: 85.47\n",
+ "Pearson: 0.073\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR, \n",
+ " \"plain20-random-l1_rank\",\n",
+ " \"2019.07.21-004045\")\n",
+ "\n",
+ "df_baseline6 = pd.read_csv(os.path.join(fpath, \"ft_6epoch_results.csv\"))\n",
+ "df_baseline20 = pd.read_csv(os.path.join(fpath, \"ft_20epoch_results.csv\"))\n",
+ "#df_baseline60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
+ "\n",
+ " \n",
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " xlabel=\"Fine-tuned Top1 Accuracy\",\n",
+ " ylabel=\"Search Top1 Accuracy\")\n",
+ "for df in (df_baseline, df_baseline6, df_baseline20): #, df_baseline60):\n",
+ " plot_networks2(df)\n",
+ " print(\"Pearson: %.3f\" % pearsonr(df.top1, df.search_top1)[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What happens when we randomly sample Resnet20 (instead of Plain20) networks?\n",
+ "\n",
+ "<code>\n",
+ "time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/resnet20-random-reconstruction_nondeterministic amc.py --arch=resnet20_cifar ${CIFAR10_PATH} --resume=../../ssl/checkpoints/checkpoint_trained_dense.pth.tar --lr=0.05 --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=Random-policy --amc-cfg=auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=random -j=1\n",
+ "</code>\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " time python ../../classifier_compression/parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/resnet20-random-reconstruction_nondeterministic/2019.07.31-213149 --arch=resnet20_cifar --lr=0.1 --vs=0 -p=50 --epochs=60 --compress=../plain20_fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic --output-csv=ft_60epoch_results.csv --processes=16"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We see so many high-scoring networks that we discover randomly!<br>\n",
+ "A (uniform) random policy performs surpisingly well, but the Pearson corralation is not very high. This means that we'll need to sample and fine-tune quite a few of the high-performing search-top1 networks, to find the best performing converged network.\n",
+ "\n",
+ "We can also [graph the filter-pruning sensitivity](https://github.com/NervanaSystems/distiller/blob/master/jupyter/sensitivity_analysis.ipynb) of ResNet20 vs. Plain20 vs. ResNet50 and see that of the three models, Plain20 is most sensitive to filter pruning, while the other two are quite robust."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best network: 90.98\n",
+ "Pearson: 0.755\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR, \n",
+ " \"resnet20-random-reconstruction_nondeterministic\",\n",
+ " \"2019.07.31-213149\")\n",
+ "\n",
+ "df_baseline60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
+ "\n",
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " xlabel=\"Fine-tuned Top1 Accuracy\",\n",
+ " ylabel=\"Search Top1 Accuracy\")\n",
+ "plot_networks2(df_baseline60)\n",
+ "print(\"Pearson: %.3f\" % pearsonr(df_baseline60.top1, df_baseline60.search_top1)[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.7"
+ },
+ "pycharm": {
+ "stem_cell": {
+ "cell_type": "raw",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": []
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/auto_compression/amc/jupyter/amc_resnet20.ipynb b/examples/auto_compression/amc/jupyter/amc_resnet20.ipynb
new file mode 100644
index 0000000..4124e14
--- /dev/null
+++ b/examples/auto_compression/amc/jupyter/amc_resnet20.ipynb
@@ -0,0 +1,245 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Resnet 20 - CIFAR10\n",
+ "\n",
+ "## Comparing multiple executions\n",
+ "\n",
+ "* How stable is the ranking of discovered networks?\n",
+ " \n",
+ "\n",
+ "Let's run 4 instances of AMC (ResNet20, DDPG) and then review the results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd \n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib \n",
+ "import csv\n",
+ "from matplotlib.ticker import FuncFormatter\n",
+ "import ipywidgets as widgets\n",
+ "from ipywidgets import interactive, interact, Layout\n",
+ "import matplotlib.pylab as pylab\n",
+ "import matplotlib.animation as animation\n",
+ "from matplotlib import animation, rc\n",
+ "\n",
+ "from auto_compression_jupyter import *\n",
+ "\n",
+ "#plt.style.use('seaborn') # pretty matplotlib plots\n",
+ "\n",
+ "params = {'legend.fontsize': 'x-large',\n",
+ " 'figure.figsize': (15, 7),\n",
+ " 'axes.labelsize': 'x-large',\n",
+ " 'axes.titlesize':'xx-large',\n",
+ " 'xtick.labelsize':'x-large',\n",
+ " 'ytick.labelsize':'x-large'}\n",
+ "pylab.rcParams.update(params)\n",
+ "\n",
+ " \n",
+ "# Widen the cells to get entire rows in the screen.\n",
+ "from IPython.core.display import display, HTML\n",
+ "#display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
+ "\n",
+ "EXPERIMENTS_DIR = os.path.join(\"/experiments\", \"amc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fpath = os.path.join(EXPERIMENTS_DIR, \"resnet20-ddpg-private\", \"2019.07.30-015225\")\n",
+ "df_list = load_experiment_instances(fpath)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multiple experiment runs\n",
+ "\n",
+ "Below we plot the execution of the same experiment several times.<br>\n",
+ "We configure AMC as follows:\n",
+ "- Architecture: Resnet20 (CIFAR10)\n",
+ "- Action range: 5% - 100% retain rate\n",
+ "- Target density: 50%\n",
+ "- Don't fine-tune a discovered network before providing a reward.\n",
+ "\n",
+ "The command-line is provided below:\n",
+ " \n",
+ " time python3 ../../classifier_compression/multi-run.py ${AMC_EXP_PATH}/resnet20-ddpg-private amc.py --arch=resnet20_cifar ${CIFAR10_PATH} --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=fm-reconstruction --amc-agent-algo=DDPG --amc-cfg=auto_compression_channels.yaml --amc-rllib=private -j=1\n",
+ " \n",
+ "Each colored line represents one experiment execution instance. We plot the search-Top1 score of discovered networks as the RL-based AMC system learns to find better compressed networks. You might be impressed by:\n",
+ "* The variability in behavior, which is typical for RL algorithms.\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "dda486527296439991c54fd9dd6cb493",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), IntSlider(value=0, descr…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_experiment_comparison(df_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's train some networks to convergence. We look at all of the networks that were progressively discovered as \"best\" networks during AMC's search. Note that these are **not** the best scoring discovered-networks.\n",
+ "\n",
+ "Fine-tune for 60 epochs (using 16 processes): **notice the large LR**.\n",
+ "\n",
+ " time python parallel-finetune.py --scan-dir=${AMC_EXP_PATH}/resnet20-ddpg-private/2019.07.30-015225 --arch=resnet20_cifar --lr=0.1 --vs=0 -p=50 --compress=../automated_deep_compression/fine_tune.yaml ${CIFAR10_PATH} -j=1 --epochs=60 --output-csv=ft_60epoch_results.csv --processes=16"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Bubbles represent discovered networks\n",
+ "* Bubble sizes represent the relative number of FLOPs in each network\n",
+ "* Darker networks were discovered later in the search process "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best network: 91.26\n",
+ "Pearson: 0.751\n",
+ " exp search_top1 top1 name\n",
+ "39 2 77.08 91.26 BEST_adc_episode_138_checkpoint.pth.tar\n",
+ "56 3 75.60 91.25 BEST_adc_episode_207_checkpoint.pth.tar\n",
+ "60 3 77.04 91.16 BEST_adc_episode_555_checkpoint.pth.tar\n",
+ "62 3 77.56 91.11 BEST_adc_episode_587_checkpoint.pth.tar\n",
+ "66 3 78.24 91.10 BEST_adc_episode_637_checkpoint.pth.tar\n",
+ "71\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_resnet20_ft60 = pd.read_csv(os.path.join(fpath, \"ft_60epoch_results.csv\"))\n",
+ "\n",
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "shorten_dir_name(df_resnet20_ft60)\n",
+ "plot_networks(df_resnet20_ft60, edgecolors='orange')\n",
+ "print(len(df_resnet20_ft60.index))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at the same diagram from a different perspective.<br>\n",
+ "Remember, we ran our AMC experiment 4 times, and we fine-tuned networks discovered in each of these 4 experiments. Now let's separate these networks by color and observe how they cluster."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "create_fig(\"Discovered Networks:\\nComparing Search Top1 vs Fine-tuned Top1\\n\",\n",
+ " \"Fine-tuned Top1 Accuracy\",\n",
+ " \"Search Top1 Accuracy\")\n",
+ "plot_networks_by_experiment(df_resnet20_ft60, edgecolors='red')#, create_figure=False)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.7"
+ },
+ "pycharm": {
+ "stem_cell": {
+ "cell_type": "raw",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": []
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/auto_compression/amc/jupyter/amc_worksheet.ipynb b/examples/auto_compression/amc/jupyter/amc_worksheet.ipynb
new file mode 100644
index 0000000..5df4c55
--- /dev/null
+++ b/examples/auto_compression/amc/jupyter/amc_worksheet.ipynb
@@ -0,0 +1,701 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "-"
+ }
+ },
+ "source": [
+ "# AMC Worksheet\n",
+ "\n",
+ "\n",
+ "Use this notebook to load AMC experiments to do a deep-dive analysis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Notebook code\n",
+ "\n",
+ "Skip this part - it is necessary only for creating the diagrams. You may also toggle the code-view button."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import HTML\n",
+ "HTML('''<script>\n",
+ "code_show=true; \n",
+ "function code_toggle() {\n",
+ " if (code_show){\n",
+ " $('div.input').hide();\n",
+ " } else {\n",
+ " $('div.input').show();\n",
+ " }\n",
+ " code_show = !code_show\n",
+ "} \n",
+ "$( document ).ready(code_toggle);\n",
+ "</script>\n",
+ "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off code view\"></form>''')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## Analyze AMC Experiment\n",
+ "\n",
+ "\n",
+ " - [Read the results log file](#Read-the-results-log-files)\n",
+ " - [Plot experiment performance](#Plot-experiment-performance)\n",
+ " - [Compare multiple experiments](#Compare-experiments)\n",
+ " - [Sample best networks](#Sample-best-networks)\n",
+ " - [PCA projection](#Two-dimesional-PCA-projection)\n",
+ " - [Plot action distributions](#These-are-the-actions-the-networks-experienced)\n",
+ " - [Plot the learning process projected to 2D-space](#Network-2D-embeddings)\n",
+ " - [Plot actions distributions](#Plot-a-histogram-of-the-actions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "from amc_worksheet_jupyter import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read the results log files\n",
+ "\n",
+ "The code below reads the log file of your selected experiment. To change the path to the file you will need to open the code cell and change its content."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/experiments/amc/plain20-ddpg-private/2019.08.01-181040/1___2019.08.01-181047/amc.csv\n"
+ ]
+ }
+ ],
+ "source": [
+ "#pd.set_option('display.max_colwidth', 150)\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "EXPERIMENTS_DIR = os.path.join(\"/experiments\", \"amc\")\n",
+ "\n",
+ "# expr_dir = os.path.join(EXPERIMENTS_DIR,\n",
+ "# \"mobilenet_v1_training___2019.06.30-084753-ddpg-reconstruction\",\n",
+ "# \"2019.08.02-023635\")\n",
+ "# fpath = os.path.join(expr_dir, \"0___2019.08.02-023641\")\n",
+ "# fpath = os.path.join(expr_dir, \"1___2019.08.02-023641\")\n",
+ "# fpath = os.path.join(expr_dir, \"2___2019.08.02-023641\")\n",
+ "# fpath = os.path.join(expr_dir, \"3___2019.08.02-023641\")\n",
+ "\n",
+ "expr_dir = os.path.join(EXPERIMENTS_DIR,\n",
+ " \"plain20-ddpg-private\",\n",
+ " \"2019.08.01-181040\")\n",
+ "#fpath = os.path.join(expr_dir, \"0___2019.08.01-181048\")\n",
+ "fpath = os.path.join(expr_dir, \"1___2019.08.01-181047\")\n",
+ "# fpath = os.path.join(expr_dir, \"2___2019.08.01-181047\")\n",
+ "# fpath = os.path.join(expr_dir, \"3___2019.08.01-181047\")\n",
+ "\n",
+ "fname = os.path.join(fpath, \"amc.csv\")\n",
+ "print(fname)\n",
+ "df = pd.read_csv(fname)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot experiment performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": false,
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "26ea8560c6314af6a1c409ab99cba3d5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), Checkbox(value=True, des…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "@interact(window_size=(0,50,5), top1=True, macs=True, params=False, reward=True)\n",
+ "def plot_performance_proxy(window_size=10, top1=True, macs=True, params=False, reward=True):\n",
+ " plot_performance(\"Training Data\", df, 0.15, window_size, top1, macs, params, reward)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compare experiments\n",
+ "\n",
+ "If you have multiple experiments, you can compare them here."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "e624f7ed3e4442f2b3367617135b19a5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), IntSlider(value=0, descr…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import auto_compression_jupyter as acj\n",
+ "\n",
+ "df_list = acj.load_experiment_instances(expr_dir)\n",
+ "df_len = min([len(df) for df in df_list])\n",
+ "\n",
+ "@interact(window_size=(0,50,5), top1=True, macs=True, params=False, reward=True, zoom=(0,df_len,1))\n",
+ "def plot_experiment_comparison(window_size=10, zoom=0):\n",
+ " start = 0\n",
+ " end = zoom if zoom > 0 else 0\n",
+ " acj.plot_performance(\"Compare Experiments (Top1)\", df_list, \n",
+ " 0.15, window_size, True, False, False, False, start, end, plot_type='compare')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a4310d8ebed4467e9dddcebabceeb839",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), Output()), _dom_classes=…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "@interact(window_size=(0,50,5), top1=True, macs=True, params=False, reward=True)\n",
+ "def plot_experiment_comparison(window_size=10):\n",
+ " plot_performance(\"Compare Experiments (Top1)\\nError view\", df_list, \n",
+ " 0.15, window_size, True, False, False, False, plot_type='error')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "### Sample best networks\n",
+ "\n",
+ "Let's look at the networks with the best top1 accuracy, and see if they share geometrical attributes.\n",
+ "\n",
+ "We sort the discovered networks by their search-Top1 accuracy and display the density of each layer in the networks. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unique: 8\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import hashlib\n",
+ "from collections import OrderedDict\n",
+ "\n",
+ "# Choose network to study\n",
+ "df = df\n",
+ "top1_sorted_df = df.sort_values(by=['reward'], ascending=False)\n",
+ "\n",
+ "\n",
+ "nrows = 2; ncols = 4\n",
+ "f, axarr = plt.subplots(nrows, ncols, figsize=(15,7))\n",
+ "arch_dict = OrderedDict()\n",
+ "arch_shapes_dict = OrderedDict()\n",
+ "for i in range(0, nrows * ncols):\n",
+ " record = top1_sorted_df.iloc[i]\n",
+ " episode = record['episode']\n",
+ " net_layer_sizes = json.loads(record[\"performance\"])\n",
+ " #print(\"{}\".format(type(list(net_layer_sizes.values())[0])))\n",
+ " net_as_nparray = np.array(list(net_layer_sizes.values()), dtype=np.float32)\n",
+ " arch_shapes_dict[episode] = net_as_nparray\n",
+ " net_str = \",\".join(str(layer_size) for layer_size in net_layer_sizes.values())\n",
+ " net_hash = hashlib.sha1(net_str.encode())\n",
+ " try: \n",
+ " arch_dict[net_hash].append(episode)\n",
+ " except KeyError:\n",
+ " arch_dict[net_hash] = [episode]\n",
+ " \n",
+ " plot_layer_compute_densities(top1_sorted_df, i, ax=axarr[i//ncols, i%ncols], color='g')\n",
+ " # Fine-tune figure; make subplots farther from each other.\n",
+ " f.subplots_adjust(hspace=0.6, wspace=0.4)\n",
+ " \n",
+ "print(\"Unique: \", len(arch_dict))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Two-dimesional PCA projection\n",
+ "\n",
+ "We can get a notion of the degree of similarities of the discovered networks, but projecting their architectures onto a lower-dimensional space, using PCA projection.\n",
+ "To do this we convert each network architecture to a numpy array, where each coefficient in the array is the normalized number of weight elements left in the layer's weights (i.e. this is the density of the layer weights)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Which components explain the variance best [0.63029018 0.22002047]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.cm as cm\n",
+ "from sklearn.decomposition import PCA\n",
+ "\n",
+ "\n",
+ "def net2nparray(record):\n",
+ " net_layer_sizes = json.loads(record[\"performance\"])\n",
+ " net_as_nparray = np.array(list(net_layer_sizes.values()), dtype=np.float32)\n",
+ " return net_as_nparray\n",
+ "\n",
+ "def collect_info(df):\n",
+ " arch_shapes_dict = OrderedDict()\n",
+ " for i in range(len(df.index)):\n",
+ " record = top1_sorted_df.iloc[i]\n",
+ " episode = record['episode'] \n",
+ " arch_shapes_dict[episode] = net2nparray(record)\n",
+ " return arch_shapes_dict\n",
+ "\n",
+ "def pca_projection(arch_shapes_dict, title, show_legend, show_episode_ids):\n",
+ " pca = PCA(n_components=2)\n",
+ " principal_components = pca.fit_transform(list(arch_shapes_dict.values()))\n",
+ " #episodes = [str(episode) for episode in arch_shapes_dict.keys()]\n",
+ " pc_df = pd.DataFrame(data=principal_components, columns = ['pc1', 'pc2'])\n",
+ " #pc_df = pd.concat([pc_df, pd.Series(episodes)], axis = 1)\n",
+ "\n",
+ " fig = plt.figure(figsize = (10,10))\n",
+ " ax = fig.add_subplot(1,1,1) \n",
+ " ax.set_xlabel('Principal Component 1', fontsize=15)\n",
+ " ax.set_ylabel('Principal Component 2', fontsize=15)\n",
+ " ax.set_title(title, fontsize=20)\n",
+ "\n",
+ " cnt_nets = len(arch_shapes_dict)\n",
+ " colors = ['lightskyblue', 'orange', 'red', 'green', 'blue', 'purple', 'pink','lime']\n",
+ "\n",
+ " episodes = list(arch_shapes_dict.keys())\n",
+ " for i in range(cnt_nets): \n",
+ " x,y = pc_df.pc1, pc_df.pc2\n",
+ " ax.scatter(x[i], y[i], s=50, label=episodes[i], \n",
+ " color=colors[episodes[i]//100], alpha=0.5)\n",
+ " if show_episode_ids:\n",
+ " ax.text(x[i], y[i], episodes[i], fontsize=9)\n",
+ " if show_legend:\n",
+ " plt.legend(labels, loc='best')\n",
+ " ax.grid()\n",
+ " print(\"Which components explain the variance best\", pca.explained_variance_ratio_)\n",
+ "\n",
+ "\n",
+ "arch_shapes_dict = collect_info(top1_sorted_df[0:10])\n",
+ "pca_projection(arch_shapes_dict,\n",
+ " title='2-Component PCA\\nBest 10 Networks',\n",
+ " show_legend=False, show_episode_ids=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at the projection of the entire set of discovered networks. Each 100 episodes are color-coded using a different color.\n",
+ "\n",
+ "We can see how AMC follows a \"discovery manifold\" and slowly searches and discovers \"better\" networks in the manifold."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Which components explain the variance best [0.41691629 0.25621805]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "arch_shapes_dict = collect_info(top1_sorted_df)\n",
+ "pca_projection(arch_shapes_dict,\n",
+ " title='2-Component PCA\\nAll Networks',\n",
+ " show_legend=False, show_episode_ids=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### These are the actions the networks experienced"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "scrolled": false,
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "top1_sorted_df = df.sort_values(by=['reward'], ascending=False)\n",
+ "nrows = 2; ncols = 4\n",
+ "f, axarr = plt.subplots(nrows, ncols, figsize=(15,7))\n",
+ "for i in range(0, nrows * ncols):\n",
+ " plot_action_history(top1_sorted_df, i, ax=axarr[i//ncols, i%ncols], color='b')\n",
+ " # Fine-tune figure; make subplots farther from each other.\n",
+ " f.subplots_adjust(hspace=0.6, wspace=0.4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Per-layer filter density distribution - top 10% networks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x540 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "top10pct = top1_sorted_df[:int(len(df.index) * 0.1)]\n",
+ "#top10pct = df[int(len(df.index) * 0.95):]\n",
+ "\n",
+ "layer_densities_list = []\n",
+ "for index, row in top10pct.iterrows():\n",
+ " layer_densities = json.loads(row['performance'])\n",
+ " layer_densities = list(layer_densities.values())\n",
+ "\n",
+ " #net_performance = json.loads(record[\"performance\"])\n",
+ " #ax.bar(range(len(net_performance)), list(net_performance.values()), color=color, align='center')\n",
+ "\n",
+ " #layer_densities = [1. - float(sparsity) for sparsity in layer_sparsities]\n",
+ " layer_densities_list.append(layer_densities)\n",
+ "\n",
+ "layer_densities = np.array(layer_densities_list)\n",
+ "mean = layer_densities.mean(axis=0)\n",
+ "std = layer_densities.std(axis=0)\n",
+ "\n",
+ "\n",
+ "# Draw the bar diagram of the layer densities\n",
+ "fig, ax = plt.subplots(figsize=(15,7.5))\n",
+ "ax.set_title(\"Best sampled models (90th percentile)\\nPer-layer filter density (mean and std)\")\n",
+ "xpos = [i for i in range(len(mean))]\n",
+ "ax.bar(xpos, mean, yerr=std, capsize=10, alpha=0.50, ecolor='black')\n",
+ "ax.set_ylabel('Filter density')\n",
+ "ax.set_xticks(xpos)\n",
+ "layer_names = json.loads(row['performance']).keys()\n",
+ "ax.set_xticklabels(layer_names, rotation=270)\n",
+ "ax.yaxis.grid(True)\n",
+ "#plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Network 2D embeddings\n",
+ "\n",
+ "Let's create an embedding of the networks AMC discovers over the course of each experiment session. Each network is projected onto a 2D plane mapping the Top1 accuracy versus the compute budget, and is represented by a small circle. I used gradient-color-coding to show the relative phase where each network is discovered. Lighter circles are networks discovered early in the search, darker networks are discovered later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "top1 = df['top1']\n",
+ "normalized_macs = df['normalized_macs']\n",
+ "plot_2d_embeddings(top1, normalized_macs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Video animation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "a = AnimatedScatter(normalized_macs, top1)\n",
+ "plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
+ "plt.xlabel('Normalized MACs')\n",
+ "plt.ylabel('Top1 Accuracy')\n",
+ "#a.ani.save('amc_vgg16.mp4', fps=10, dpi=80) #Frame per second controls speed, dpi controls the quality \n",
+ "rc('animation', html='html5')\n",
+ "a.ani"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot a histogram of the actions\n",
+ "\n",
+ "We want to look at the distribution of the sampled agent actions (```agent_actions``` below), to make sure they are not skewed or biased.\n",
+ "\n",
+ "We also want to compare these actions to the actions the environment actually acts on (these we call ```env_actions``` below). \n",
+ "For Clipped-PPO this is extra-interesting: The natural PPO action-space is (-inf, inf) so the environment needs to scale and shift the agent actions to fit into the \"real\" action-space."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_all_actions(df, action_type='action_history'):\n",
+ " actions = []\n",
+ " for index, record in df.iterrows():\n",
+ " #layer_sparsities = record[action_type]\n",
+ " #layer_sparsities = layer_sparsities[1:-1].split(\",\")\n",
+ " layer_sparsities = json.loads(record[action_type])\n",
+ " layer_sparsities = [float(sparsity) for sparsity in layer_sparsities]\n",
+ " actions.extend(layer_sparsities)\n",
+ " return actions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "14400\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x540 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "agent_actions = get_all_actions(df, action_type='agent_action_history')\n",
+ "env_actions = get_all_actions(df, action_type='action_history')\n",
+ "print(len(agent_actions))\n",
+ "\n",
+ "plt.figure(figsize=[15,7.5])\n",
+ "plt.hist(agent_actions, histtype='step', bins=100, label='agent_actions (u={:.2f} std={:.2f})'.format(\n",
+ " np.mean(agent_actions),\n",
+ " np.std(agent_actions)));\n",
+ "plt.hist(env_actions, histtype='step', bins=100, label='env_actions (u={:.2f} std={:.2f})'.format(\n",
+ " np.mean(env_actions),\n",
+ " np.std(env_actions)));\n",
+ "\n",
+ "plt.title('Action histogram (actions as seen by the environment)')\n",
+ "plt.legend()\n",
+ "plt.xlabel('Action value')\n",
+ "plt.ylabel('Frequency');"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/auto_compression/amc/jupyter/amc_worksheet_jupyter.py b/examples/auto_compression/amc/jupyter/amc_worksheet_jupyter.py
new file mode 100644
index 0000000..73fedf7
--- /dev/null
+++ b/examples/auto_compression/amc/jupyter/amc_worksheet_jupyter.py
@@ -0,0 +1,286 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib
+import csv
+from matplotlib.ticker import FuncFormatter
+import ipywidgets as widgets
+from ipywidgets import interactive, interact, Layout
+import matplotlib.pylab as pylab
+import matplotlib.animation as animation
+from matplotlib import animation, rc
+
+params = {'legend.fontsize': 'x-large',
+ 'figure.figsize': (15, 7),
+ 'axes.labelsize': 'x-large',
+ 'axes.titlesize': 'xx-large',
+ 'xtick.labelsize': 'x-large',
+ 'ytick.labelsize': 'x-large'}
+pylab.rcParams.update(params)
+
+
+# plt.style.use('seaborn') # pretty matplotlib plots
+
+
+def to_percent(y, position):
+ # Ignore the passed in position. This has the effect of scaling the default
+ # tick locations.
+ if y < 1:
+ y = 100 * y
+ s = "{:.1f}".format(y)
+
+ # The percent symbol needs escaping in latex
+ if matplotlib.rcParams['text.usetex'] is True:
+ return s + r'$\%$'
+ else:
+ return s + '%'
+
+
+# Widen the cells to get entire rows in the screen.
+from IPython.core.display import display, HTML
+# display(HTML("<style>.container { width:100% !important; }</style>"))
+
+import json
+
+
+def plot_layer_compute_densities(df, idx, ax=None, color=None):
+ if ax is None:
+ plt.figure()
+ ax = plt
+
+ record = df.iloc[idx]
+ net_performance = json.loads(record["performance"])
+ ax.bar(range(len(net_performance)), list(net_performance.values()), color=color, align='center')
+ ax.set_title("Ep:{} - Top1:{:.1f}%\nMACs:{:.1f}%".format(record['episode'],
+ record['top1'],
+ record['normalized_macs']))
+ # ax.set_xticks(range(len(net_performance)), list(net_performance.keys()))
+
+
+def plot_action_history(df, idx, action_type='action_history', ax=None, color=None):
+ if ax is None:
+ plt.figure()
+ ax = plt
+
+ record = df.iloc[idx]
+ layer_sparsities = json.loads(record[action_type])
+ # layer_sparsities = record[action_type]
+ # layer_sparsities = layer_sparsities[1:-1].split(",")
+ layer_densities = [1. - float(sparsity) for sparsity in layer_sparsities]
+ ax.bar(range(len(layer_densities)), layer_densities, color=color)
+ ax.set_title("Ep:{} - Top1:{:.1f}%\nMACs:{:.1f}%".format(record['episode'],
+ record['top1'],
+ record['normalized_macs']))
+
+
+def smooth(data, win_size):
+ if not win_size:
+ return data
+ win_size = max(0, win_size)
+ return [np.mean(data[max(0, i - win_size):i]) for i in range(len(data))]
+
+
+def plot_performance(title, dfs, alpha, window_size, top1, macs, params, reward, start=0, end=-1, plot_type='error'):
+ plot_kwargs = {"figsize": (15, 7), "lw": 1, "alpha": alpha, "title": title, "grid": True}
+ smooth_kwargs = {"lw": 2 if window_size > 0 else 1, "legend": True, "grid": True}
+
+ if not isinstance(dfs, list):
+ dfs = [dfs]
+ # Apply zoom
+ df_end = min([len(df) for df in dfs])
+ if end > 0:
+ # df_end = min(df_end, end)
+ end = min(df_end, end)
+ else:
+ end = df_end
+ print(end)
+ # dfs = [df[:df_end].copy() for df in dfs]
+ dfs = [df for df in dfs]
+ df = dfs[0]
+ left_axs, right_axs = [], []
+
+ if macs:
+ ax = df['normalized_macs'][start:end].plot(**plot_kwargs, color="r")
+ left_axs.append((ax, "MACs"))
+ # ax.set(xlabel="Episode", ylabel="(%)")
+ # ax.set_ylim([0,100])
+ df['smooth_normalized_macs'] = smooth(df['normalized_macs'], window_size)
+ df['smooth_normalized_macs'][start:end].plot(**smooth_kwargs, color="r")
+
+ if top1:
+ for df in dfs:
+ df['smooth_top1'] = smooth(df['top1'], window_size)
+
+ if len(dfs) > 1:
+ plot_kwargs['alpha'] = 1.0
+ plot_kwargs['legend'] = True
+ if plot_type == 'error':
+ top1_len = min([len(df) for df in dfs])
+ dfs = [df[:top1_len] for df in dfs]
+ dfs_top1 = [df['smooth_top1'] for df in dfs]
+ dfs_top1_dp = pd.DataFrame(dfs_top1)
+ top1_min = dfs_top1_dp.min(axis=0)
+ top1_max = dfs_top1_dp.max(axis=0)
+ top1_mean = dfs_top1_dp.mean(axis=0)
+
+ display_mean = False
+ if display_mean:
+ ax = top1_mean.plot(**plot_kwargs, color="b")
+
+ for p in dfs_top1:
+ ax = p[start:end].plot(**plot_kwargs)
+ if display_mean:
+ ax.legend(['mean'] + [str(i + 1) for i in range(len(dfs_top1))])
+ else:
+ ax.legend([str(i + 1) for i in range(len(dfs_top1))])
+ ax.set(xlabel="Episode", ylabel="(%)")
+ ax.fill_between(range(len(top1_min)), top1_max, top1_min, color="b", alpha=0.3)
+
+ left_axs.append((ax, "Top1"))
+ else:
+ assert plot_type == 'compare'
+ dfs_top1 = [df['smooth_top1'] for df in dfs]
+ for p in dfs_top1:
+ # ax = p[start:end].plot(**plot_kwargs)
+ ax = p[start:].plot(**plot_kwargs)
+ ax.legend([str(i + 1) for i in range(len(dfs_top1))])
+ left_axs.append((ax, "Top1"))
+ else:
+ ax = df['top1'][start:end].plot(**plot_kwargs, color="b")
+ left_axs.append((ax, "Top1"))
+ # ax.set(xlabel="Episode", ylabel="(%)")
+ df['smooth_top1'][start:end].plot(**smooth_kwargs, color="b")
+
+ if params:
+ ax = df['normalized_nnz'][start:end].plot(**plot_kwargs, color="black")
+ ax.set(xlabel="Episode", ylabel="(%)")
+ df['smooth_normalized_nnz'] = smooth(df['normalized_nnz'], window_size)
+ df['smooth_normalized_nnz'][start:end].plot(**smooth_kwargs, color="black")
+
+ if reward:
+ ax = df['reward'][start:end].plot(**plot_kwargs, secondary_y=True, color="g")
+ ax.set(xlabel="Episode", ylabel="Reward")
+ df['smooth_reward'] = smooth(df['reward'], window_size)
+ df['smooth_reward'][start:end].plot(**smooth_kwargs, secondary_y=True, color="g")
+ # ax.set_ylim([0,100])
+ # ax.grid(True, which='minor', axis='x', alpha=0.3)
+ plt.xlabel("Episode")
+
+ # The left axis might contain multiple ylabels
+ if left_axs:
+ # Pick an arbitrary axis
+ ax = left_axs[0][0]
+ # Collect all of the labels
+ ylabel = " / ".join([ax[1] for ax in left_axs]) # left_axs[0][1]
+ ax.set(ylabel=ylabel)
+
+
+def plot_2d_embeddings(top1, normalized_macs):
+ plt.figure(figsize=(15, 7))
+ plt.title('Projection of Discovered Networks ({})'.format(len(top1)))
+ plt.xlabel('Normalized MACs')
+ plt.ylabel('Top1 Accuracy')
+
+ # Create the formatter using the function to_percent. This multiplies all the
+ # default labels by 100, making them all percentages
+ formatter = FuncFormatter(to_percent)
+
+ # Set the formatter
+ plt.gca().yaxis.set_major_formatter(formatter)
+ plt.gca().xaxis.set_major_formatter(formatter)
+
+ # Use color gradients to show the "age" of the network:
+ # Lighter networks were discovered earlier than darker ones.
+ color_grad = [str(1 - i / len(top1)) for i in range(len(top1))]
+ plt.scatter(normalized_macs, top1, color=color_grad, s=80, edgecolors='gray');
+
+
+INTERVAL = 30 # Animation speed
+WINDOW = 20
+
+font = {'family': 'serif',
+ 'color': 'darkred',
+ 'weight': 'normal',
+ 'alpha': 0.50,
+ 'size': 32,
+ }
+
+
+# Based on these two helpful example code:
+# https://stackoverflow.com/questions/9401658/how-to-animate-a-scatter-plot
+# http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/.
+# Specifically, the use of IPython.display is missing from the first example, but most of the animation code
+# leverages code from there.
+class AnimatedScatter(object):
+ """An animated scatter plot using matplotlib.animations.FuncAnimation."""
+
+ def __init__(self, xdata, ydata):
+ assert len(xdata) == len(ydata)
+ self.numpoints = len(xdata)
+ self.xdata = xdata
+ self.ydata = ydata
+ self.stream = self.data_stream()
+
+ # Setup the figure and axes...
+ self.fig, self.ax = plt.subplots(figsize=(15, 7))
+ # Then setup FuncAnimation.
+ self.ani = animation.FuncAnimation(self.fig, self.update, interval=INTERVAL,
+ frames=self.numpoints - 2,
+ init_func=self.setup_plot, blit=True)
+
+ def setup_plot(self):
+ """Initialize drawing of the scatter plot."""
+ x, y, s, c = next(self.stream)
+ # self.annot = self.ax.annotate("txt", (10, 10))
+ self.scat = self.ax.scatter(x, y, c=c, s=s, animated=False)
+ self.scat.set_edgecolors('gray')
+ self.scat.set_cmap('gray')
+ self.width = max(self.xdata) - min(self.xdata) + 4
+ self.height = max(self.ydata) - min(self.ydata) + 4
+ self.ax.axis([min(self.xdata) - 2, max(self.xdata) + 2,
+ min(self.ydata) - 2, max(self.ydata) + 2])
+
+ self.annot = self.ax.text(min(self.xdata) + self.width / 2, self.height / 2,
+ "", fontdict=font)
+ # For FuncAnimation's sake, we need to return the artist we'll be using
+ # Note that it expects a sequence of artists, thus the trailing comma.
+ return self.scat,
+
+ def data_stream(self):
+ numpoints = 0 # len(self.xdata)
+ colors = []
+ xxx = 0
+ while True:
+ numpoints += 1
+ win_len = min(WINDOW, numpoints)
+ data = np.ndarray((4, win_len))
+ start = max(0, numpoints - WINDOW - 1)
+ data[0, :] = self.xdata[start:start + win_len]
+ data[1, :] = self.ydata[start:start + win_len]
+ data[2, :] = [70] * win_len # point size
+ # data[3, :] = [np.random.random() for p in range(numpoints)] # color
+ # The color of the points is a gradient with larger values for "younger" points.
+ # At each new frame we show one more point, and "age" each existing point by incrementaly
+ # reducing its color gradient.
+ data[3, :] = [(1 - i / (win_len + 1)) for i in range(win_len)]
+ yield data
+
+ def update(self, i):
+ """Update the scatter plot."""
+ data = next(self.stream)
+ self.annot.set_text(i)
+ i = i % len(data)
+
+ # Set x and y data
+ xy = [(data[0, i], data[1, i]) for i in range(len(data[0, :]))]
+ self.scat.set_offsets(xy)
+
+ # Set colors
+ self.scat.set_array(data[3])
+
+ # We need to return the updated artist for FuncAnimation to draw..
+ # Note that it expects a sequence of artists, thus the trailing comma.
+ return self.scat, self.annot
+
+ def show(self):
+ plt.show()
\ No newline at end of file
diff --git a/examples/auto_compression/amc/jupyter/auto_compression_jupyter.py b/examples/auto_compression/amc/jupyter/auto_compression_jupyter.py
new file mode 100755
index 0000000..35f73e3
--- /dev/null
+++ b/examples/auto_compression/amc/jupyter/auto_compression_jupyter.py
@@ -0,0 +1,328 @@
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib
+import csv
+from matplotlib.ticker import FuncFormatter
+import ipywidgets as widgets
+from ipywidgets import interactive, interact, Layout
+import matplotlib.pylab as pylab
+import matplotlib.animation as animation
+from matplotlib import animation, rc
+from scipy.stats.stats import pearsonr
+
+
+def to_percent(y, position):
+ # Ignore the passed in position. This has the effect of scaling the default
+ # tick locations.
+ if y < 1:
+ y = 100 * y
+ s = "{:.1f}".format(y)
+
+ # The percent symbol needs escaping in latex
+ if matplotlib.rcParams['text.usetex'] is True:
+ return s + r'$\%$'
+ else:
+ return s + '%'
+
+import json
+
+def plot_layer_compute_densities(df, idx, ax=None, color=None):
+ if ax is None:
+ plt.figure()
+ ax = plt
+
+ record = df.iloc[idx]
+ net_performance = json.loads(record["performance"])
+ ax.bar(range(len(net_performance)), list(net_performance.values()), color=color, align='center')
+ ax.set_title("Ep:{} - Top1:{:.1f}%\nMACs:{:.1f}%".format(record['episode'],
+ record['top1'],
+ record['normalized_macs']))
+ #ax.set_xticks(range(len(net_performance)), list(net_performance.keys()))
+
+
+def plot_action_history(df, idx, action_type='action_history', ax=None, color=None):
+ if ax is None:
+ plt.figure()
+ ax = plt
+
+ record = df.iloc[idx]
+ layer_sparsities = json.loads(record[action_type])
+ #layer_sparsities = record[action_type]
+ #layer_sparsities = layer_sparsities[1:-1].split(",")
+ layer_densities = [1.- float(sparsity) for sparsity in layer_sparsities]
+ ax.bar(range(len(layer_densities)), layer_densities, color=color)
+ ax.set_title("Ep:{} - Top1:{:.1f}%\nMACs:{:.1f}%".format(record['episode'],
+ record['top1'],
+ record['normalized_macs']))
+
+
+def smooth(data, win_size):
+ if not win_size:
+ return data
+ win_size = max(0, win_size)
+ return [np.mean(data[max(0, i-win_size):i]) for i in range(len(data))]
+
+
+def plot_performance(title, dfs, alpha, window_size, top1, macs, params, reward, start=0, end=-1, plot_type='error'):
+ plot_kwargs = {"figsize":(15,7), "lw": 1, "alpha": alpha, "title": title, "grid": True}
+ smooth_kwargs = {"lw": 2 if window_size > 0 else 1, "legend": True, "grid": True}
+
+ if not isinstance(dfs, list):
+ dfs = [dfs]
+ # Apply zoom
+ df_end = min([len(df) for df in dfs])
+ if end > 0:
+ #df_end = min(df_end, end)
+ end = min(df_end, end)
+ else:
+ end = df_end
+ print(end)
+ #dfs = [df[:df_end].copy() for df in dfs]
+ dfs = [df for df in dfs]
+ df = dfs[0]
+ left_axs, right_axs = [], []
+
+ if macs:
+ ax = df['normalized_macs'][start:end].plot(**plot_kwargs, color="r")
+ left_axs.append((ax, "MACs"))
+ #ax.set(xlabel="Episode", ylabel="(%)")
+ #ax.set_ylim([0,100])
+ df['smooth_normalized_macs'] = smooth(df['normalized_macs'], window_size)
+ df['smooth_normalized_macs'][start:end].plot(**smooth_kwargs, color="r")
+
+ if top1:
+ for df in dfs:
+ df['smooth_top1'] = smooth(df['top1'], window_size)
+
+ if len(dfs) > 1:
+ plot_kwargs['alpha'] = 1.0
+ plot_kwargs['legend'] = True
+ if plot_type == 'error':
+ top1_len = min([len(df) for df in dfs])
+ dfs = [df[:top1_len] for df in dfs]
+ dfs_top1 = [df['smooth_top1'] for df in dfs]
+ dfs_top1_dp = pd.DataFrame(dfs_top1)
+ top1_min = dfs_top1_dp.min(axis=0)
+ top1_max = dfs_top1_dp.max(axis=0)
+ top1_mean = dfs_top1_dp.mean(axis=0)
+
+ display_mean = False
+ if display_mean:
+ ax = top1_mean.plot(**plot_kwargs, color="b")
+
+ for p in dfs_top1:
+ ax = p[start:end].plot(**plot_kwargs)
+ if display_mean:
+ ax.legend(['mean'] + [str(i) for i in range(len(dfs_top1))])
+ else:
+ ax.legend([str(i) for i in range(len(dfs_top1))])
+ ax.set(xlabel="Episode", ylabel="(%)")
+ ax.fill_between(range(len(top1_min)), top1_max, top1_min, color="b", alpha=0.3)
+
+ left_axs.append((ax, "Top1"))
+ else:
+ assert plot_type == 'compare'
+ dfs_top1 = [df['smooth_top1'] for df in dfs]
+ for p in dfs_top1:
+ #ax = p[start:end].plot(**plot_kwargs)
+ ax = p[start:].plot(**plot_kwargs)
+ ax.legend([str(i) for i in range(len(dfs_top1))])
+ left_axs.append((ax, "Top1"))
+ else:
+ ax = df['top1'][start:end].plot(**plot_kwargs, color="b")
+ left_axs.append((ax, "Top1"))
+ #ax.set(xlabel="Episode", ylabel="(%)")
+ df['smooth_top1'][start:end].plot(**smooth_kwargs, color="b")
+
+ if params:
+ ax = df['normalized_nnz'][start:end].plot(**plot_kwargs, color="black")
+ ax.set(xlabel="Episode", ylabel="(%)")
+ df['smooth_normalized_nnz'] = smooth(df['normalized_nnz'], window_size)
+ df['smooth_normalized_nnz'][start:end].plot(**smooth_kwargs, color="black")
+
+ if reward:
+ ax = df['reward'][start:end].plot(**plot_kwargs, secondary_y=True, color="g")
+ ax.set(xlabel="Episode", ylabel="Reward")
+ df['smooth_reward'] = smooth(df['reward'], window_size)
+ df['smooth_reward'][start:end].plot(**smooth_kwargs, secondary_y=True, color="g")
+ #ax.set_ylim([0,100])
+ #ax.grid(True, which='minor', axis='x', alpha=0.3)
+ plt.xlabel("Episode")
+
+ # The left axis might contain multiple ylabels
+ if left_axs:
+ # Pick an arbitrary axis
+ ax = left_axs[0][0]
+ # Collect all of the labels
+ ylabel = " / ".join([ax[1] for ax in left_axs]) #left_axs[0][1]
+ ax.set(ylabel=ylabel)
+
+
+def plot_2d_embeddings(top1, normalized_macs):
+ plt.figure(figsize=(15,7))
+ plt.title('Projection of Discovered Networks ({})'.format(len(top1)))
+ plt.xlabel('Normalized MACs')
+ plt.ylabel('Top1 Accuracy')
+
+ # Create the formatter using the function to_percent. This multiplies all the
+ # default labels by 100, making them all percentages
+ formatter = FuncFormatter(to_percent)
+
+ # Set the formatter
+ plt.gca().yaxis.set_major_formatter(formatter)
+ plt.gca().xaxis.set_major_formatter(formatter)
+
+ # Use color gradients to show the "age" of the network:
+ # Lighter networks were discovered earlier than darker ones.
+ color_grad = [str(1-i/len(top1)) for i in range(len(top1))]
+ plt.scatter(normalized_macs, top1, color=color_grad, s=80, edgecolors='gray');
+
+
+INTERVAL = 30 # Animation speed
+WINDOW = 20
+
+font = {'family': 'serif',
+ 'color': 'darkred',
+ 'weight': 'normal',
+ 'alpha': 0.50,
+ 'size': 32,
+ }
+
+# Based on these two helpful example code:
+# https://stackoverflow.com/questions/9401658/how-to-animate-a-scatter-plot
+# http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/.
+# Specifically, the use of IPython.display is missing from the first example, but most of the animation code
+# leverages code from there.
+class AnimatedScatter(object):
+ """An animated scatter plot using matplotlib.animations.FuncAnimation."""
+ def __init__(self, xdata, ydata):
+ assert len(xdata) == len(ydata)
+ self.numpoints = len(xdata)
+ self.xdata = xdata
+ self.ydata = ydata
+ self.stream = self.data_stream()
+
+ # Setup the figure and axes...
+ self.fig, self.ax = plt.subplots(figsize=(15,7))
+ # Then setup FuncAnimation.
+ self.ani = animation.FuncAnimation(self.fig, self.update, interval=INTERVAL,
+ frames=self.numpoints-2,
+ init_func=self.setup_plot, blit=True)
+
+ def setup_plot(self):
+ """Initialize drawing of the scatter plot."""
+ x, y, s, c = next(self.stream)
+ #self.annot = self.ax.annotate("txt", (10, 10))
+ self.scat = self.ax.scatter(x, y, c=c, s=s, animated=False)
+ self.scat.set_edgecolors('gray')
+ self.scat.set_cmap('gray')
+ self.width = max(self.xdata) - min(self.xdata) + 4
+ self.height = max(self.ydata) - min(self.ydata) + 4
+ self.ax.axis([min(self.xdata)-2, max(self.xdata)+2,
+ min(self.ydata)-2, max(self.ydata)+2])
+
+ self.annot = self.ax.text(min(self.xdata) + self.width/2, self.height/2,
+ "", fontdict=font)
+ # For FuncAnimation's sake, we need to return the artist we'll be using
+ # Note that it expects a sequence of artists, thus the trailing comma.
+ return self.scat,
+
+ def data_stream(self):
+ numpoints = 0#len(self.xdata)
+ colors = []
+ xxx = 0
+ while True:
+ numpoints += 1
+ win_len = min(WINDOW, numpoints)
+ data = np.ndarray((4, win_len))
+ start = max(0,numpoints-WINDOW-1)
+ data[0, :] = self.xdata[start:start+win_len]
+ data[1, :] = self.ydata[start:start+win_len]
+ data[2, :] = [70] * win_len # point size
+ #data[3, :] = [np.random.random() for p in range(numpoints)] # color
+ # The color of the points is a gradient with larger values for "younger" points.
+ # At each new frame we show one more point, and "age" each existing point by incrementaly
+ # reducing its color gradient.
+ data[3, :] = [(1-i/(win_len+1)) for i in range(win_len)]
+ yield data
+
+ def update(self, i):
+ """Update the scatter plot."""
+ data = next(self.stream)
+ self.annot.set_text(i)
+ i = i % len(data)
+
+ # Set x and y data
+ xy = [(data[0,i], data[1,i]) for i in range(len(data[0,:]))]
+ self.scat.set_offsets(xy)
+
+ # Set colors
+ self.scat.set_array(data[3])
+
+ # We need to return the updated artist for FuncAnimation to draw..
+ # Note that it expects a sequence of artists, thus the trailing comma.
+ return self.scat, self.annot
+
+ def show(self):
+ plt.show()
+
+
+def get_immediate_subdirs(a_dir):
+ return [os.path.join(a_dir, name) for name in os.listdir(a_dir)
+ if os.path.isdir(os.path.join(a_dir, name)) and name != "ft"]
+
+
+def load_experiment_instances(dir_with_experiments):
+ experiment_instance_dirs = get_immediate_subdirs(dir_with_experiments)
+ return [pd.read_csv(os.path.join(dirname, "amc.csv")) for dirname in experiment_instance_dirs]
+
+
+def plot_experiment_comparison(df_list):
+ df_len = min([len(df) for df in df_list])
+ @interact(window_size=(0,50,5), top1=True, macs=True, params=False, reward=True, zoom=(0,df_len,1))
+ def _plot_experiment_comparison(window_size=10, zoom=0):
+ start = 0
+ end = zoom if zoom > 0 else 0
+ plot_performance("Compare AMC Experiment Executions (Top1)", df_list,
+ 0.15, window_size, True, False, False, False, start, end, plot_type='compare')
+
+
+def shorten_dir_name(df):
+ df_grouped = df.groupby('dir')
+ df['dir'] = df['dir'].map(lambda x: x[len("experiments/plain20-ddpg-private/"):])
+ df['exp'] = df['dir'].map(lambda x: x.split("___")[0][-1])
+
+
+def create_fig(title, xlabel, ylabel):
+ plt.figure(figsize=(15, 7))
+ plt.grid(True)
+ # plt.legend()
+ plt.title(title)
+ plt.xlabel(xlabel)
+ plt.ylabel(ylabel)
+
+
+def plot_networks(df, edgecolors):
+ size = df.macs / max(df.macs) * 100
+ plt.scatter(df.top1, df.search_top1, s=size, c=df.index, edgecolors=edgecolors, cmap='gray')
+ print("Best network: %.2f" % max(df.top1))
+ print("Pearson: %.3f" % pearsonr(df.top1, df.search_top1)[0])
+ df_sorted = df.sort_values(by=['top1'], inplace=False, ascending=False)
+ # Five best
+ print(df_sorted[:5][['exp', 'search_top1', 'top1', 'name']]) # [('name','top1')])
+
+
+def plot_networks_by_experiment(df, edgecolors, create_figure=True):
+ # Group by experiment directory
+ df_grouped = df.groupby('exp')
+ size = df.macs / max(df.macs) * 100
+ legend = []
+ for exp_dir, df_experiment in df_grouped:
+ a = plt.scatter(df_experiment.top1, df_experiment.search_top1, s=size,
+ edgecolors=edgecolors, alpha=0.5)
+ p = pearsonr(df_experiment.top1, df_experiment.search_top1)[0]
+ legend.append((a, "%s: Best Top1=%.2f Pearson: %.3f" % (exp_dir, max(df_experiment.top1), p)))
+ plots, labels = zip(*legend)
+ plt.legend(plots, labels)
\ No newline at end of file
diff --git a/examples/auto_compression/amc/logging.conf b/examples/auto_compression/amc/logging.conf
new file mode 100755
index 0000000..67875df
--- /dev/null
+++ b/examples/auto_compression/amc/logging.conf
@@ -0,0 +1,60 @@
+[formatters]
+keys: simple, time_simple
+
+[handlers]
+keys: console, file
+
+[loggers]
+keys: root, app_cfg, distiller.thinning, apputils.model_summaries, examples.auto_compression.amc
+
+[formatter_simple]
+format: %(message)s
+
+[formatter_time_simple]
+format: %(asctime)s - %(message)s
+
+[handler_console]
+class: StreamHandler
+propagate: 0
+args: []
+formatter: simple
+
+[handler_file]
+class: FileHandler
+mode: 'w'
+args=('%(logfilename)s', 'w')
+formatter: time_simple
+
+[logger_root]
+level: INFO
+propagate: 1
+handlers: console, file
+
+[logger_app_cfg]
+# Use this logger to log the application configuration and execution environment
+level: DEBUG
+qualname: app_cfg
+propagate: 0
+handlers: file
+
+# Example of adding a module-specific logger
+# Do not forget to add distiller.thinning to the list of keys in section [loggers]
+[logger_distiller.thinning]
+level: INFO
+qualname: distiller.thinning
+propagate: 0
+handlers: console, file
+
+# Example of adding a module-specific logger
+# Do not forget to add apputils.model_summaries to the list of keys in section [loggers]
+[logger_apputils.model_summaries]
+level: INFO
+qualname: apputils.model_summaries
+propagate: 0
+handlers: console, file
+
+[logger_examples.auto_compression.amc]
+level: INFO
+qualname: examples.auto_compression.amc
+propagate: 0
+handlers: console, file
diff --git a/examples/auto_compression/amc/parallel-finetune.py b/examples/auto_compression/amc/parallel-finetune.py
new file mode 100755
index 0000000..80c834f
--- /dev/null
+++ b/examples/auto_compression/amc/parallel-finetune.py
@@ -0,0 +1,243 @@
+#
+# Copyright (c) 2019 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""A script to fine-tune a directory of classifier checkpoints, using multiple processes
+running in parallel.
+
+Format:
+ $ python parallel-run.py --scan-dir=<directory containing the output of multi-run.py>
+ --ft-epochs=<number of epochs to tine-tune> --output-csv=<CSV output file> <fine-tuning command-line>
+Example:
+ $ time python parallel-finetune.py --scan-dir=experiments/plain20-random-l1_rank/2019.07.21-004045/ --ft-epochs=3 --output-csv=ft_1epoch_results.csv --arch=plain20_cifar --lr=0.005 --vs=0 -p=50 --epochs=60 --compress=../automated_deep_compression/fine_tune.yaml ${CIFAR10_PATH} -j=1 --deterministic
+"""
+
+import os
+import glob
+import math
+import shutil
+import torch
+import torch.multiprocessing as multiprocessing
+from torch.multiprocessing import Process, set_start_method
+import pandas as pd
+from collections import OrderedDict
+import csv
+import distiller.apputils.image_classifier as classifier
+
+
+class _CSVLogger(object):
+ def __init__(self, fname, headers):
+ """Create the CSV file and write the column names"""
+ with open(fname, 'w') as f:
+ writer = csv.writer(f)
+ writer.writerow(headers)
+ self.fname = fname
+
+ def add_record(self, fields):
+ # We close the file each time to flush on every write, and protect against data-loss on crashes
+ with open(self.fname, 'a') as f:
+ writer = csv.writer(f)
+ writer.writerow(fields)
+ f.flush()
+
+
+class FTStatsLogger(_CSVLogger):
+ def __init__(self, fname):
+ headers = ['dir', 'name', 'macs', 'search_top1', 'top1', 'top5', 'loss']
+ super().__init__(fname, headers)
+
+
+class FinetuningTask(object):
+ def __init__(self, args):
+ self.args = args
+
+ def __call__(self, data_loader):
+ return finetune_checkpoint(*self.args, data_loader)
+
+
+# Boiler-plat code (src: https://pymotw.com/2/multiprocessing/communication.html)
+class FinetuningProcess(Process):
+ def __init__(self, task_queue, result_queue, data_loader):
+ multiprocessing.Process.__init__(self)
+ self.task_queue = task_queue
+ self.result_queue = result_queue
+ self.data_loader = data_loader
+
+ def run(self):
+ proc_name = self.name
+ while True:
+ next_task = self.task_queue.get()
+ if next_task is None:
+ print('%s: Exiting' % proc_name)
+ self.task_queue.task_done()
+ break
+
+ print('executing on %s: %s' % (proc_name, next_task))
+ answer = next_task(self.data_loader)
+ self.task_queue.task_done()
+ self.result_queue.put(answer)
+ return
+
+
+def finetune_directory(ft_dir, stats_file, app_args, cleanup_ft_dir=False, checkpoints=None):
+ """Fine tune all the checkpoint files we find in the immediate-directory specified.
+
+ For each checkpoint file we find, we create and queue a FinetuningTask.
+ A FinetuningProcess will pickup the FinetuningTask and process it.
+ """
+ print("Fine-tuning directory %s" % ft_dir)
+ if not checkpoints:
+ # Get a list of the checkpoint files
+ checkpoints = glob.glob(os.path.join(ft_dir, "*checkpoint.pth.tar"))
+ assert checkpoints
+
+ # We create a subdirectory, where we will write all of our output
+ ft_output_dir = os.path.join(ft_dir, "ft")
+ os.makedirs(ft_output_dir, exist_ok=True)
+ print("Writing results to directory %s" % ft_output_dir)
+ app_args.output_dir = ft_output_dir
+
+ # Multi-process queues
+ tasks = multiprocessing.JoinableQueue()
+ results = multiprocessing.Queue()
+
+ # Create and launch the fine-tuning processes
+ processes = []
+ n_processes = min(app_args.processes, len(checkpoints))
+ for i in range(n_processes):
+ # Pre-load the data-loaders of each fine-tuning process once
+ app = classifier.ClassifierCompressor(app_args, script_dir=os.path.dirname(__file__))
+ data_loader = classifier.load_data(app.args)
+ # Delete log directories
+ shutil.rmtree(app.logdir)
+ processes.append(FinetuningProcess(tasks, results, data_loader))
+ # Start the process
+ processes[-1].start()
+
+ n_gpus = torch.cuda.device_count()
+
+ # Enqueue all of the fine-tuning tasks
+ for (instance, ckpt_file) in enumerate(checkpoints):
+ tasks.put(FinetuningTask(args=(ckpt_file, instance%n_gpus, app_args)))
+
+ # Push an end-of-tasks marker
+ for i in range(len(processes)):
+ tasks.put(None)
+
+ # Wait until all tasks finish
+ tasks.join()
+
+ # Start printing results
+ results_dict = OrderedDict()
+ while not results.empty():
+ result = results.get()
+ results_dict[result[0]] = result[1]
+
+ # Read the results of the AMC experiment (we'll want to use some of the data)
+ import pandas as pd
+ df = pd.read_csv(os.path.join(ft_dir, "amc.csv"))
+ assert len(results_dict) > 0
+ # Log some info for each checkpoint
+ for ckpt_name in sorted (results_dict.keys()):
+ net_search_results = df[df["ckpt_name"] == ckpt_name[:-len("_checkpoint.pth.tar")]]
+ search_top1 = net_search_results["top1"].iloc[0]
+ normalized_macs = net_search_results["normalized_macs"].iloc[0]
+ log_entry = (ft_output_dir, ckpt_name, normalized_macs,
+ search_top1, *results_dict[ckpt_name])
+ print("%s <> %s: %.2f %.2f %.2f %.2f %.2f" % log_entry)
+ stats_file.add_record(log_entry)
+ if cleanup_ft_dir:
+ # cleanup: remove the "ft" directory
+ shutil.rmtree(ft_output_dir)
+
+
+def finetune_checkpoint(ckpt_file, gpu, app_args, loaders):
+ # Usually when we train, we also want to look at and graph, the validation score of each epoch.
+ # When we run many fine-tuning sessions at once, we don't care to look at the validation score.
+ # However, we want to perform a sort-of "early-stopping" in which we use the checkpoint of the
+ # best performing training epoch, and not the checkpoint created by the last epoch.
+ # We evaluate what is the "best" checkpoint by looking at the validation accuracy
+ name = os.path.basename(ckpt_file)
+ print("Fine-tuning checkpoint %s" % name)
+
+ app_args.gpus = str(gpu)
+ app_args.name = name
+ app_args.deprecated_resume = ckpt_file
+ app = classifier.ClassifierCompressor(app_args, script_dir=os.path.dirname(__file__))
+ app.train_loader, app.val_loader, app.test_loader = loaders
+ best = [float("-inf"), float("-inf"), float("inf")]
+ for epoch in range(app_args.epochs):
+ validate = epoch >= math.floor((1-app_args.validate_enable_factor) * app_args.epochs)
+ top1, top5, loss = app.train_validate_with_scheduling(epoch, validate=validate, verbose=False)
+ if validate:
+ if top1 > best[0]:
+ best = [top1, top5, loss]
+ if app_args.validate_enable_factor == 0:
+ # We did not validate, so our score is the performance on the Test dataset
+ return (name, app.test())
+ return (name, best)
+
+
+if __name__ == '__main__':
+ def get_immediate_subdirs(a_dir):
+ subdirs = [os.path.join(a_dir, name) for name in os.listdir(a_dir)
+ if os.path.isdir(os.path.join(a_dir, name)) and name != "ft"]
+ subdirs.sort()
+ return subdirs
+
+
+ def add_parallel_args(argparser):
+ group = argparser.add_argument_group('parallel fine-tuning')
+ group.add_argument('--processes', type=int, default=4,
+ help="Number of parallel experiment processes to run in parallel")
+ group.add_argument('--scan-dir', metavar='DIR', required=True, help='path to checkpoints')
+ group.add_argument('--output-csv', metavar='DIR', required=True,
+ help='Name of the CSV file containing the output')
+ group.add_argument('--top-performing-chkpts', action='store_true', default=False,
+ help='Fine tune only the best performing discovered checkpoints (sorted by search-Top1)')
+ group.add_argument('--validate-enable-factor', type=float, default=0.2,
+ help="What portion of the epochs to validate (0=never validate; 1=validate after every "
+ "epoch; 0<factor<1 validate the last factor*ags.epochs epcohs."
+ "In the latter case, the reported score for the fine-tuned model is the "
+ "best performing validation score.")
+ return argparser
+
+
+ def get_best_checkpoints(ft_dir, best_nets=5):
+ df_amc_results = pd.read_csv(os.path.join(ft_dir, "amc.csv"))
+ top1_sorted_df_amc_results = df_amc_results.sort_values(by=['top1'], ascending=False)
+ top1_sorted_df_amc_results = top1_sorted_df_amc_results[0:best_nets]
+ checkpoints = [os.path.join(ft_dir, ckpt + "_checkpoint.pth.tar")
+ for ckpt in top1_sorted_df_amc_results.ckpt_name]
+ return checkpoints
+
+
+ try:
+ set_start_method('forkserver')
+ except RuntimeError:
+ pass
+ # Parse arguments
+ argparser = classifier.init_classifier_compression_arg_parser()
+ add_parallel_args(argparser)
+ app_args = argparser.parse_args()
+
+ print("Starting fine-tuning")
+ stats_file = FTStatsLogger(os.path.join(app_args.scan_dir, app_args.output_csv))
+ ft_dirs = get_immediate_subdirs(app_args.scan_dir)
+ for ft_dir in ft_dirs:
+ checkpoints = None
+ if app_args.top_performing_chkpts:
+ checkpoints = get_best_checkpoints(ft_dir)
+ finetune_directory(ft_dir, stats_file, app_args, checkpoints=checkpoints)
diff --git a/examples/auto_compression/amc/rewards.py b/examples/auto_compression/amc/rewards.py
new file mode 100755
index 0000000..480ee1b
--- /dev/null
+++ b/examples/auto_compression/amc/rewards.py
@@ -0,0 +1,111 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import logging
+import math
+
+
+msglogger = logging.getLogger("examples.auto_compression.amc")
+
+
+def reward_factory(reward_name):
+ """Returns a reward function and a function with logic to clamp an action.
+
+ This pair is defines the --amc-protocol choice.
+ """
+ return {
+ "mac-constrained": (amc_mac_constrained_reward_fn, mac_constrained_clamp_action),
+ "accuracy-guaranteed": (amc_accuracy_guarantee_reward_fn, None),
+ "mac-constrained-experimental": (mac_constrained_experimental_reward_fn, None),
+ "harmonic-mean": (harmonic_mean_reward_fn, None),
+ "punish-agent": (punish_agent_reward_fn, None)
+ }[reward_name]
+
+
+def amc_mac_constrained_reward_fn(env, top1, top5, vloss, total_macs):
+ return top1/100
+
+
+def amc_accuracy_guarantee_reward_fn(env, top1, top5, vloss, total_macs):
+ return -(1-top1/100) * math.log(total_macs)
+
+
+def mac_constrained_experimental_reward_fn(env, top1, top5, vloss, total_macs):
+ """A more intuitive reward for constraining the compute and optimizing the
+ accuracy under this constraint.
+ """
+ macs_normalized = total_macs/env.original_model_macs
+ reward = top1/100
+ if macs_normalized > (env.amc_cfg.target_density+0.002):
+ reward = -3 - macs_normalized
+ else:
+ reward += 1
+ return reward
+
+
+def mac_constrained_clamp_action(env, pruning_action):
+ """Compute a resource-constrained action"""
+
+ # Todo: this is tightly coupled to the environment - refactor
+ flops = env.net_wrapper.layer_macs(env.current_layer())
+ assert flops > 0
+ reduced = env._removed_macs
+ prunable_rest, rest = env.rest_macs_raw()
+ rest += prunable_rest * env.action_high # how much we have to remove in other layers
+ target_reduction = (1 - env.amc_cfg.target_density) * env.original_model_macs
+ assert reduced == env.original_model_macs - env.net_wrapper.total_macs
+ duty = target_reduction - (reduced + rest)
+ pruning_action_final = min(env.action_high, max(pruning_action, duty/flops))
+
+ msglogger.debug("\t\tflops=%.3f reduced=%.3f rest=%.3f duty=%.3f" % (flops, reduced, rest, duty))
+ msglogger.debug("\t\tpruning_action=%.3f pruning_action_final=%.3f" % (pruning_action, pruning_action_final))
+ msglogger.debug("\t\ttarget={:.2f} reduced={:.2f} rest={:.2f} duty={:.2f} flops={:.2f}".
+ format( 1-env.amc_cfg.target_density, reduced/env.original_model_macs,
+ rest/env.original_model_macs,
+ duty/env.original_model_macs,
+ flops/env.original_model_macs))
+ if pruning_action_final != pruning_action:
+ msglogger.debug("action ********** pruning_action={}==>pruning_action_final={:.2f}: reduced={:.2f} rest={:.2f} target={:.2f} duty={:.2f} flops={:.2f}".
+ format(pruning_action, pruning_action_final, reduced/env.original_model_macs,
+ rest/env.original_model_macs, 1-env.amc_cfg.target_density,
+ duty/env.original_model_macs,
+ flops/env.original_model_macs))
+ return pruning_action_final
+
+
+def harmonic_mean_reward_fn(env, top1, top5, vloss, total_macs):
+ """This reward is based on the idea of weighted harmonic mean
+
+ Balance compute and accuracy provided a beta value that weighs the two components.
+ See: https://en.wikipedia.org/wiki/F1_score
+ """
+ beta = 1
+ #beta = 0.75 # How much to favor accuracy
+ macs_normalized = total_macs/env.original_model_macs
+ reward = (1 + beta**2) * top1/100 * macs_normalized / (beta**2 * macs_normalized + top1/100)
+ return reward
+
+
+def punish_agent_reward_fn(env, top1, top5, vloss, total_macs):
+ """This reward punishes the agent when it produces networks that don't comply with the MACs resource-constraint,
+ (the negative reward is in proportion to the network density). Otherwise, the reward is the Top1 accuracy.
+ """
+ if not env.is_macs_constraint_achieved(total_macs):
+ current_density = total_macs / env.original_model_macs
+ reward = env.amc_cfg.target_density - current_density
+ else:
+ reward = top1/100
+ return reward
diff --git a/examples/auto_compression/amc/rl_libs/__init__.py b/examples/auto_compression/amc/rl_libs/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/auto_compression/amc/rl_libs/coach/__init__.py b/examples/auto_compression/amc/rl_libs/coach/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/auto_compression/amc/rl_libs/coach/coach_if.py b/examples/auto_compression/amc/rl_libs/coach/coach_if.py
new file mode 100755
index 0000000..41e2804
--- /dev/null
+++ b/examples/auto_compression/amc/rl_libs/coach/coach_if.py
@@ -0,0 +1,77 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from rl_coach.base_parameters import TaskParameters
+from rl_coach.core_types import EnvironmentSteps, EnvironmentEpisodes, TrainingSteps
+from rl_coach.schedules import ConstantSchedule, PieceWiseSchedule, ExponentialSchedule
+from rl_coach.memories.memory import MemoryGranularity
+import logging
+import os
+
+msglogger = logging.getLogger()
+
+
+class RlLibInterface(object):
+ """Interface to the Coach RL algorithm framework"""
+
+ def solve(self, model, app_args, amc_cfg, services, steps_per_episode):
+ msglogger.info("AMC: Using coach")
+
+ # When we import the graph_manager from the ADC_DDPG preset, we implicitly instruct
+ # Coach to create and use our DistillerWrapperEnvironment environment.
+ # So Distiller calls Coach, which creates the environment, trains the agent, and ends.
+ if amc_cfg.agent_algo == "DDPG":
+ from examples.auto_compression.amc.rl_libs.coach.presets.ADC_DDPG import (graph_manager,
+ agent_params)
+ graph_manager.agent_params.exploration.noise_schedule = ExponentialSchedule(amc_cfg.ddpg_cfg.initial_training_noise,
+ 0,
+ amc_cfg.ddpg_cfg.training_noise_decay)
+ # Number of iterations to train
+ graph_manager.agent_params.algorithm.num_consecutive_training_steps = steps_per_episode
+ #graph_manager.agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(1)
+ graph_manager.agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(steps_per_episode)
+ # Heatup
+ graph_manager.heatup_steps = EnvironmentEpisodes(amc_cfg.ddpg_cfg.num_heatup_episodes)
+ # Replay buffer size
+ graph_manager.agent_params.memory.max_size = (MemoryGranularity.Transitions, amc_cfg.ddpg_cfg.replay_buffer_size)
+ elif "ClippedPPO" in amc_cfg.agent_algo:
+ from examples.automated_deep_compression.rl_libs.coach.presets.ADC_ClippedPPO import graph_manager, agent_params
+ elif "TD3" in amc_cfg.agent_algo:
+ from examples.automated_deep_compression.rl_libs.coach.presets.ADC_TD3 import graph_manager, agent_params
+ else:
+ raise ValueError("The agent algorithm you are trying to use (%s) is not supported" % amc_cfg.amc_agent_algo)
+
+ # Number of training steps
+ n_training_episodes = amc_cfg.ddpg_cfg.num_training_episodes
+ graph_manager.improve_steps = EnvironmentEpisodes(n_training_episodes)
+ # Don't evaluate until the end
+ graph_manager.steps_between_evaluation_periods = EnvironmentEpisodes(n_training_episodes)
+
+ # These parameters are passed to the Distiller environment
+ env_cfg = {'model': model,
+ 'app_args': app_args,
+ 'amc_cfg': amc_cfg,
+ 'services': services}
+ graph_manager.env_params.additional_simulator_parameters = env_cfg
+
+ coach_logs_dir = os.path.join(msglogger.logdir, 'coach')
+ os.mkdir(coach_logs_dir)
+ task_parameters = TaskParameters(experiment_path=coach_logs_dir)
+ # Set Coach's PRNG seed
+ if app_args.seed is not None:
+ task_parameters.seed = app_args.seed
+ graph_manager.create_graph(task_parameters)
+ graph_manager.improve()
\ No newline at end of file
diff --git a/examples/automated_deep_compression/presets/ADC_ClippedPPO.py b/examples/auto_compression/amc/rl_libs/coach/presets/ADC_ClippedPPO.py
similarity index 98%
rename from examples/automated_deep_compression/presets/ADC_ClippedPPO.py
rename to examples/auto_compression/amc/rl_libs/coach/presets/ADC_ClippedPPO.py
index 4f1e788..9c222ba 100755
--- a/examples/automated_deep_compression/presets/ADC_ClippedPPO.py
+++ b/examples/auto_compression/amc/rl_libs/coach/presets/ADC_ClippedPPO.py
@@ -47,6 +47,7 @@ agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
agent_params.algorithm.beta_entropy = 0
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = 1
+# How many epochs to train the network using supervised methods
agent_params.algorithm.optimization_epochs = 10
agent_params.algorithm.estimate_state_value_using_gae = True
diff --git a/examples/automated_deep_compression/presets/ADC_DDPG.py b/examples/auto_compression/amc/rl_libs/coach/presets/ADC_DDPG.py
similarity index 59%
rename from examples/automated_deep_compression/presets/ADC_DDPG.py
rename to examples/auto_compression/amc/rl_libs/coach/presets/ADC_DDPG.py
index d7e9523..5397ebd 100755
--- a/examples/automated_deep_compression/presets/ADC_DDPG.py
+++ b/examples/auto_compression/amc/rl_libs/coach/presets/ADC_DDPG.py
@@ -6,21 +6,20 @@ from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
from rl_coach.environments.gym_environment import GymVectorEnvironment
from rl_coach.exploration_policies.truncated_normal import TruncatedNormalParameters
from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
-
-from rl_coach.memories.memory import MemoryGranularity
from rl_coach.base_parameters import EmbedderScheme
from rl_coach.architectures.tensorflow_components.layers import Dense
-
-steps_per_episode = 13
+from rl_coach.base_parameters import EmbeddingMergerType
+from rl_coach.filters.filter import InputFilter
+# !!!! Enable when using branch "distiller-AMC-induced-changes"
+from rl_coach.filters.reward import RewardEwmaNormalizationFilter
+import numpy as np
####################
-# Block Scheduling #
+# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
-schedule_params.improve_steps = EnvironmentEpisodes(800)
-schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(5)
-schedule_params.evaluation_steps = EnvironmentEpisodes(1) # Neta: 0
-schedule_params.heatup_steps = EnvironmentEpisodes(100)
+schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(0)
+schedule_params.evaluation_steps = EnvironmentEpisodes(0)
#####################
# DDPG Agent Params #
@@ -31,46 +30,53 @@ agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(300
agent_params.network_wrappers['actor'].heads_parameters[0].activation_function = 'sigmoid'
agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(300)]
agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(300)]
-agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = EmbedderScheme.Empty
+agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = [Dense(300)]
+
+agent_params.network_wrappers['critic'].embedding_merger_type = EmbeddingMergerType.Sum
agent_params.network_wrappers['actor'].optimizer_type = 'Adam'
agent_params.network_wrappers['actor'].adam_optimizer_beta1 = 0.9
agent_params.network_wrappers['actor'].adam_optimizer_beta2 = 0.999
+agent_params.network_wrappers['actor'].optimizer_epsilon = 1e-8
+#agent_params.network_wrappers['actor'].wd = 0
agent_params.network_wrappers['critic'].optimizer_type = 'Adam'
agent_params.network_wrappers['critic'].adam_optimizer_beta1 = 0.9
agent_params.network_wrappers['critic'].adam_optimizer_beta2 = 0.999
+agent_params.network_wrappers['critic'].optimizer_epsilon = 1e-8
+agent_params.network_wrappers['actor'].learning_rate = 1e-4
+agent_params.network_wrappers['critic'].learning_rate = 1e-3
-agent_params.network_wrappers['actor'].learning_rate = 0.0001 # 0.0001
-agent_params.network_wrappers['critic'].learning_rate = 0.001
-# agent_params.network_wrappers['critic'].clip_gradients = 100
-# agent_params.network_wrappers['actor'].clip_gradients = 100
+# !!!! Enable when using branch "distiller-AMC-induced-changes"
+agent_params.algorithm.override_episode_rewards_with_the_last_transition_reward = True
agent_params.algorithm.rate_for_copying_weights_to_target = 0.01 # Tau pg. 11
-agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
-agent_params.algorithm.heatup_using_network_decisions = True
+#agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
+agent_params.algorithm.heatup_using_network_decisions = False # We want uniform-random samples during heatup
agent_params.algorithm.discount = 1
+agent_params.algorithm.use_non_zero_discount_for_terminal_states = True
-# Shadi
-agent_params.algorithm.use_non_zero_discount_for_terminal_states = True # <===========
-
-# See : https://nervanasystems.github.io/coach/components/agents/policy_optimization/ddpg.html?highlight=ddpg#rl_coach.agents.ddpg_agent.DDPGAlgorithmParameters
-# Replay buffer size
-agent_params.memory.max_size = (MemoryGranularity.Transitions, 2000)
-#agent_params.exploration = AdditiveNoiseParameters() #
agent_params.exploration = TruncatedNormalParameters()
+#agent_params.exploration = AdditiveNoiseParameters()
+agent_params.exploration.noise_as_percentage_from_action_space = False
+agent_params.exploration.evaluation_noise = 0 # Neta new
agent_params.algorithm.use_target_network_for_evaluation = True
-#agent_params.exploration.evaluation_noise_percentage = 0.01 # Neta new
-agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
+agent_params.algorithm.act_for_full_episodes = True
+
+# !!!! Enable when using branch "distiller-AMC-induced-changes"
+agent_params.algorithm.update_pre_network_filters_state_on_train = True
+agent_params.algorithm.update_pre_network_filters_state_on_inference = False
+# !!!! Enable when using branch "distiller-AMC-induced-changes"
+agent_params.pre_network_filter = InputFilter()
+agent_params.pre_network_filter.add_reward_filter('ewma_norm', RewardEwmaNormalizationFilter(alpha=0.5))
##############################
# Gym #
##############################
env_params = GymVectorEnvironment()
-env_params.level = '../automated_deep_compression/ADC.py:DistillerWrapperEnvironment'
-
+env_params.level = './environment.py:DistillerWrapperEnvironment'
vis_params = VisualizationParameters()
vis_params.dump_parameters_documentation = False
diff --git a/examples/automated_deep_compression/presets/ADC_PPO.py b/examples/auto_compression/amc/rl_libs/coach/presets/ADC_PPO.py
old mode 100644
new mode 100755
similarity index 78%
rename from examples/automated_deep_compression/presets/ADC_PPO.py
rename to examples/auto_compression/amc/rl_libs/coach/presets/ADC_PPO.py
index 26da3c5..13b204c
--- a/examples/automated_deep_compression/presets/ADC_PPO.py
+++ b/examples/auto_compression/amc/rl_libs/coach/presets/ADC_PPO.py
@@ -50,18 +50,4 @@ vis_params.dump_signals_to_csv_every_x_episodes = 1
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=vis_params)
-########
-# Test #
-########
-# preset_validation_params = PresetValidationParameters()
-# preset_validation_params.test = True
-# preset_validation_params.min_reward_threshold = 400
-# preset_validation_params.max_episodes_to_achieve_reward = 3000
-# preset_validation_params.reward_test_level = 'inverted_pendulum'
-# preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper']
-#
-# graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
-# schedule_params=schedule_params, vis_params=VisualizationParameters(),
-# preset_validation_params=preset_validation_params)
-
diff --git a/examples/auto_compression/amc/rl_libs/coach/presets/ADC_TD3.py b/examples/auto_compression/amc/rl_libs/coach/presets/ADC_TD3.py
new file mode 100755
index 0000000..3f9bd4b
--- /dev/null
+++ b/examples/auto_compression/amc/rl_libs/coach/presets/ADC_TD3.py
@@ -0,0 +1,54 @@
+from rl_coach.agents.td3_agent import TD3AgentParameters
+from rl_coach.architectures.layers import Dense
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, EmbedderScheme
+from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
+from rl_coach.environments.environment import SingleLevelSelection
+from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.exploration_policies.truncated_normal import TruncatedNormalParameters
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+
+####################
+# Graph Scheduling #
+####################
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = EnvironmentEpisodes(800)
+schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(5000)
+schedule_params.evaluation_steps = EnvironmentEpisodes(0) # Neta: 0
+schedule_params.heatup_steps = EnvironmentEpisodes(100)
+
+#########
+# Agent #
+#########
+agent_params = TD3AgentParameters()
+agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(400)]
+agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(300)]
+
+agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = EmbedderScheme.Empty
+agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = EmbedderScheme.Empty
+agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(400), Dense(300)]
+
+agent_params.exploration = TruncatedNormalParameters()
+agent_params.exploration.noise_as_percentage_from_action_space = False
+agent_params.exploration.evaluation_noise = 0 # Neta new
+agent_params.algorithm.use_target_network_for_evaluation = True
+agent_params.algorithm.act_for_full_episodes = True
+
+
+
+###############
+# Environment #
+###############
+env_params = GymVectorEnvironment()
+env_params.level = '../automated_deep_compression/ADC.py:DistillerWrapperEnvironment'
+
+
+vis_params = VisualizationParameters()
+vis_params.dump_parameters_documentation = False
+vis_params.render = True
+vis_params.native_rendering = True
+vis_params.dump_signals_to_csv_every_x_episodes = 1
+
+graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+ schedule_params=schedule_params, vis_params=vis_params)
\ No newline at end of file
diff --git a/examples/auto_compression/amc/rl_libs/coach/presets/__init__.py b/examples/auto_compression/amc/rl_libs/coach/presets/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/auto_compression/amc/rl_libs/private/__init__.py b/examples/auto_compression/amc/rl_libs/private/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/auto_compression/amc/rl_libs/private/private_if.py b/examples/auto_compression/amc/rl_libs/private/private_if.py
new file mode 100755
index 0000000..9d71601
--- /dev/null
+++ b/examples/auto_compression/amc/rl_libs/private/private_if.py
@@ -0,0 +1,54 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from examples.auto_compression.amc.rl_libs.private.agent import DDPG, train
+import logging
+
+
+msglogger = logging.getLogger()
+
+
+class ArgsContainer(object):
+ def __init__(self):
+ pass
+
+
+class RlLibInterface(object):
+ """Interface to a private DDPG impelementation."""
+
+ def solve(self, env, args):
+ msglogger.info("AMC: Using private")
+
+ agent_args = ArgsContainer()
+ agent_args.bsize = args.batch_size
+ agent_args.tau = 0.01
+ agent_args.discount = 1.
+ agent_args.epsilon = 50000
+ agent_args.init_delta = 0.5
+ agent_args.delta_decay = 0.95
+ agent_args.warmup = env.amc_cfg.ddpg_cfg.num_heatup_episodes
+ agent_args.lr_c = env.amc_cfg.ddpg_cfg.critic_lr
+ agent_args.lr_a = env.amc_cfg.ddpg_cfg.actor_lr
+ agent_args.hidden1 = 300
+ agent_args.hidden2 = 300
+ agent_args.rmsize = 100
+ agent_args.rmsize = env.amc_cfg.ddpg_cfg.replay_buffer_size
+ agent_args.window_length = 1
+ agent_args.train_episode = (env.amc_cfg.ddpg_cfg.num_heatup_episodes +
+ env.amc_cfg.ddpg_cfg.num_training_episodes)
+ agent_args.output = "."
+ agent = DDPG(args.observation_len, 1, agent_args)
+ train(agent_args.train_episode, agent, env, agent_args.output, agent_args.warmup)
diff --git a/examples/auto_compression/amc/rl_libs/random/__init__.py b/examples/auto_compression/amc/rl_libs/random/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/auto_compression/amc/rl_libs/random/random_agent.py b/examples/auto_compression/amc/rl_libs/random/random_agent.py
new file mode 100755
index 0000000..12ddc60
--- /dev/null
+++ b/examples/auto_compression/amc/rl_libs/random/random_agent.py
@@ -0,0 +1,56 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import random
+import numpy as np
+from scipy.stats import truncnorm
+import logging
+
+msglogger = logging.getLogger()
+
+
+class RandomADCActionSpace(object):
+ def __init__(self, low, high):
+ self.action_low = low
+ self.action_high = high
+
+ def sample(self):
+ return random.uniform(self.action_low, self.action_high)
+
+
+def random_agent(env):
+ """Random ADC agent"""
+ action_range = env.amc_cfg.action_range
+ best_episode = [float("-inf"), None]
+ env.action_space = RandomADCActionSpace(action_range[0], action_range[1])
+ n_episodes = env.amc_cfg.ddpg_cfg.num_training_episodes + env.amc_cfg.ddpg_cfg.num_heatup_episodes
+ for ep in range(n_episodes):
+ observation = env.reset()
+ action_config = []
+ for t in range(env.steps_per_episode):
+ #env.render(0, 0)
+ msglogger.info("[episode={}:{}] observation = {}".format(ep, t, observation))
+ # take a random action
+ action = env.action_space.sample()
+ action_config.append(action)
+ observation, reward, done, info = env.step([action])
+ if done:
+ msglogger.info("Episode finished after {} timesteps".format(t+1))
+ msglogger.info("^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^")
+ msglogger.info("New solution found: episode={} reward={} config={}".format(ep, reward, action_config))
+ break
+ if reward > best_episode[0]:
+ best_episode = (reward, action_config)
\ No newline at end of file
diff --git a/examples/auto_compression/amc/rl_libs/random/random_if.py b/examples/auto_compression/amc/rl_libs/random/random_if.py
new file mode 100755
index 0000000..61b4a63
--- /dev/null
+++ b/examples/auto_compression/amc/rl_libs/random/random_if.py
@@ -0,0 +1,29 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from .random_agent import random_agent
+import logging
+
+
+msglogger = logging.getLogger()
+
+
+class RlLibInterface(object):
+ """Interface to an agent acting using a (uniform) random policy."""
+
+ def solve(self, env):
+ msglogger.info("AMC: Using random")
+ random_agent(env)
diff --git a/examples/auto_compression/amc/rl_libs/spinningup/__init__.py b/examples/auto_compression/amc/rl_libs/spinningup/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/automated_deep_compression/ddpg/README b/examples/auto_compression/amc/rl_libs/spinningup/ddpg/README
similarity index 100%
rename from examples/automated_deep_compression/ddpg/README
rename to examples/auto_compression/amc/rl_libs/spinningup/ddpg/README
diff --git a/examples/auto_compression/amc/rl_libs/spinningup/ddpg/__init__.py b/examples/auto_compression/amc/rl_libs/spinningup/ddpg/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/automated_deep_compression/ddpg/core.py b/examples/auto_compression/amc/rl_libs/spinningup/ddpg/core.py
similarity index 62%
rename from examples/automated_deep_compression/ddpg/core.py
rename to examples/auto_compression/amc/rl_libs/spinningup/ddpg/core.py
index 8bc0e25..34a9a0a 100755
--- a/examples/automated_deep_compression/ddpg/core.py
+++ b/examples/auto_compression/amc/rl_libs/spinningup/ddpg/core.py
@@ -14,9 +14,19 @@ def mlp(x, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):
return tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activation)
# Neta
-def amc_mlp(x, a, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):
- x = tf.layers.dense(x, units=h[hidden_sizes[0]], activation=activation)
- x = tf.layers.dense(tf.concat([x, a], axis=-1), units=h[hidden_sizes[1]], activation=activation)
+def amc_actor(x):
+ x = tf.layers.dense(x, units=300, activation=tf.nn.relu)
+ x = tf.layers.dense(x, units=300, activation=tf.nn.relu)
+ return tf.layers.dense(x, units=300, activation=tf.nn.sigmoid)
+
+# Neta
+def amc_critic(x, a, hidden_sizes=(32,), activation=tf.tanh, output_activation=None):
+ x = tf.layers.dense(x, units=300, activation=tf.nn.relu)
+ a = tf.layers.dense(a, units=300, activation=tf.nn.relu)
+ y = tf.concat([x, a], axis=-1)
+ y = tf.nn.relu(y)
+ #x = tf.layers.dense(tf.concat([x, a], axis=-1), units=h[hidden_sizes[1]], activation=activation)
+ y = tf.layers.dense(y, units=300, activation=tf.nn.relu)
return tf.layers.dense(x, units=hidden_sizes[-1], activation=output_activation)
@@ -35,10 +45,11 @@ def mlp_actor_critic(x, a, hidden_sizes=(400,300), activation=tf.nn.relu,
act_dim = a.shape.as_list()[-1]
act_limit = action_space.high[0]
with tf.variable_scope('pi'):
- pi = act_limit * mlp(x, list(hidden_sizes)+[act_dim], activation, output_activation)
+ #pi = act_limit * mlp(x, list(hidden_sizes)+[act_dim], activation, output_activation)
+ pi = amc_actor(x)
with tf.variable_scope('q'):
#q = tf.squeeze(mlp(tf.concat([x,a], axis=-1), list(hidden_sizes)+[1], activation, None), axis=1)
- q = tf.squeeze(amc_mlp(x, a, list(hidden_sizes)+[1], activation, None), axis=1)
+ q = tf.squeeze(amc_critic(x, a, list(hidden_sizes)+[1], activation, None), axis=1)
with tf.variable_scope('q', reuse=True):
#q_pi = tf.squeeze(mlp(tf.concat([x,pi], axis=-1), list(hidden_sizes)+[1], activation, None), axis=1)
q_pi = tf.squeeze(mlp(x, pi, list(hidden_sizes)+[1], activation, None), axis=1)
diff --git a/examples/automated_deep_compression/ddpg/ddpg.py b/examples/auto_compression/amc/rl_libs/spinningup/ddpg/ddpg.py
similarity index 99%
rename from examples/automated_deep_compression/ddpg/ddpg.py
rename to examples/auto_compression/amc/rl_libs/spinningup/ddpg/ddpg.py
index e92747d..5ad8ae6 100755
--- a/examples/automated_deep_compression/ddpg/ddpg.py
+++ b/examples/auto_compression/amc/rl_libs/spinningup/ddpg/ddpg.py
@@ -51,7 +51,8 @@ Deep Deterministic Policy Gradient (DDPG)
def ddpg(env, test_env, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), seed=0,
steps_per_epoch=5000, epochs=100, replay_size=int(1e6), gamma=0.99,
polyak=0.995, pi_lr=1e-3, q_lr=1e-3, batch_size=100, start_steps=10000,
- act_noise=0.1, max_ep_len=1000, logger_kwargs=dict(), save_freq=1):
+ act_noise=0.1, max_ep_len=1000, logger_kwargs=dict(), save_freq=1,
+ noise_decay=0.95):
"""
Args:
@@ -255,7 +256,7 @@ def ddpg(env, test_env, actor_critic=core.mlp_actor_critic, ac_kwargs=dict(), se
# total_steps=self.amc_cfg.conv_cnt, log_freq=1, loggers=[self.tflogger])
# Neta: noise decay
if t > start_steps:
- act_noise = act_noise * 0.97
+ act_noise = act_noise * noise_decay # 0.95
# Neta: don't learn while in heatup
if t > start_steps:
diff --git a/examples/auto_compression/amc/rl_libs/spinningup/installation.md b/examples/auto_compression/amc/rl_libs/spinningup/installation.md
new file mode 100755
index 0000000..8ed0e64
--- /dev/null
+++ b/examples/auto_compression/amc/rl_libs/spinningup/installation.md
@@ -0,0 +1,29 @@
+## Spinningup installation
+
+Spinup require that we use exactly Python 3.6 so if you are not using this Python version see the instructions here:
+ http://ubuntuhandbook.org/index.php/2017/07/install-python-3-6-1-in-ubuntu-16-04-lts/
+```
+ $ sudo update-alternatives --config python3
+```
+
+For Python 3.6 you may also need to install a new virtual-env:
+```
+ $ sudo apt-get install python3.6-venv
+```
+
+Then create and activate your venv, and populate it with the Distiller packages:
+```
+ $ python3 -m venv distiller_env_python3.6
+ $ source distiller_env_python3.6/bin/activate
+ $ pip3 install -r requirements.txt
+```
+
+Finally, you need to install Spinup into this venv. First clone Spinup and then install it into your venv:
+```
+ $ cd <spinningup-repo>
+ $ sudo apt-get install python3.6-dev
+ $ pip3 install -e .
+```
+
+See also:
+https://spinningup.openai.com/en/latest/user/installation.html?highlight=license
diff --git a/examples/auto_compression/amc/rl_libs/spinningup/spinningup_if.py b/examples/auto_compression/amc/rl_libs/spinningup/spinningup_if.py
new file mode 100755
index 0000000..c102374
--- /dev/null
+++ b/examples/auto_compression/amc/rl_libs/spinningup/spinningup_if.py
@@ -0,0 +1,56 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import logging
+import tensorflow as tf
+from spinup.algos.ddpg import core
+from spinup.utils.run_utils import setup_logger_kwargs
+from .ddpg import ddpg
+
+
+msglogger = logging.getLogger()
+
+
+class RlLibInterface(object):
+ """Interface to the Spinningup RL library"""
+
+ def solve(self, env1, env2):
+ msglogger.info("AMC: Using Spinningup")
+
+ # training_noise_duration = amc_cfg.num_training_episodes * steps_per_episode
+ heatup_duration = env1.amc_cfg.ddpg_cfg.num_heatup_episodes * env1.steps_per_episode
+
+ exp_name = "Test"
+ seed = 0
+ # The number and size of the Actor-Critic MLP hidden layers
+ layers, hid = 2, 300
+ logger_kwargs = setup_logger_kwargs(exp_name)
+
+ ddpg.ddpg(env=env1, test_env=env2, actor_critic=core.mlp_actor_critic,
+ ac_kwargs=dict(hidden_sizes=[hid]*layers, output_activation=tf.sigmoid),
+ gamma=1, # discount rate
+ #seed=seed,
+ epochs=400,
+ replay_size=env1.amc_cfg.ddpg_cfg.replay_buffer_size,
+ batch_size=64,
+ start_steps=heatup_duration,
+ steps_per_epoch=env1.steps_per_episode,
+ act_noise=env1.amc_cfg.ddpg_cfg.initial_training_noise,
+ pi_lr=env1.amc_cfg.ddpg_cfg.actor_lr,
+ q_lr=env1.amc_cfg.ddpg_cfg.critic_lr,
+ logger_kwargs=logger_kwargs,
+ noise_decay=env1.amc_cfg.ddpg_cfg.training_noise_decay)
+
diff --git a/examples/auto_compression/amc/utils/__init__.py b/examples/auto_compression/amc/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/auto_compression/amc/utils/ac_loggers.py b/examples/auto_compression/amc/utils/ac_loggers.py
new file mode 100755
index 0000000..b52957a
--- /dev/null
+++ b/examples/auto_compression/amc/utils/ac_loggers.py
@@ -0,0 +1,48 @@
+#
+# Copyright (c) 2019 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import csv
+
+
+__all__ = ["AMCStatsLogger", "FineTuneStatsLogger"]
+
+
+class _CSVLogger(object):
+ def __init__(self, fname, headers):
+ """Create the CSV file and write the column names"""
+ with open(fname, 'w') as f:
+ writer = csv.writer(f)
+ writer.writerow(headers)
+ self.fname = fname
+
+ def add_record(self, fields):
+ # We close the file each time to flush on every write, and protect against data-loss on crashes
+ with open(self.fname, 'a') as f:
+ writer = csv.writer(f)
+ writer.writerow(fields)
+
+
+class AMCStatsLogger(_CSVLogger):
+ def __init__(self, fname):
+ headers = ['episode', 'top1', 'reward', 'total_macs', 'normalized_macs', 'normalized_nnz',
+ 'ckpt_name', 'action_history', 'agent_action_history', 'performance']
+ super().__init__(fname, headers)
+
+
+class FineTuneStatsLogger(_CSVLogger):
+ def __init__(self, fname):
+ headers = ['episode', 'ft_top1_list']
+ super().__init__(fname, headers)
diff --git a/examples/auto_compression/amc/utils/data_dependencies.py b/examples/auto_compression/amc/utils/data_dependencies.py
new file mode 100755
index 0000000..d82c665
--- /dev/null
+++ b/examples/auto_compression/amc/utils/data_dependencies.py
@@ -0,0 +1,70 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import logging
+import torch
+import distiller
+
+__all__ = ["find_dependencies"]
+msglogger = logging.getLogger()
+
+
+def find_dependencies(dependency_type, sgraph, layers, layer_name, dependencies_list):
+ """Produce a list of pruning-dependent layers.
+
+ Given a SummaryGraph instance (sgraph), this function returns the list of dependent
+ layers in `dependencies_list`.
+ A "dependent layer" is a layer affected by a change in the number of filters/channels
+ (dependency_type) in `layer_name`.
+ """
+ if dependency_type == "channels":
+ return _find_dependencies_channels(sgraph, layers, layer_name, dependencies_list)
+ if dependency_type == "filters":
+ return _find_dependencies_filters(sgraph, layers, layer_name, dependencies_list)
+ raise ValueError("%s is not a valid dependency type" % dependency_type)
+
+
+def _find_dependencies_channels(sgraph, layers, layer_name, dependencies_list):
+ # Find all instances of Convolution layers that immediately preceed this layer
+ predecessors = sgraph.predecessors_f(layer_name, ['Conv'])
+ for predecessor in predecessors:
+ dependencies_list.append(predecessor)
+ prev = sgraph.find_op(predecessor)
+
+ if prev['attrs']['group'] == prev['attrs']['n_ifm']:
+ # This is a group-wise convolution, and a special one at that (groups == in_channels).
+ _find_dependencies_channels(sgraph, layers, predecessor, dependencies_list)
+ elif prev['attrs']['group'] != 1:
+ raise ValueError("Distiller AutoCompression currently does not "
+ "handle this convolution groups configuration {} "
+ "(layer={} {}\n{})".format(
+ prev['attrs']['group'], predecessor, prev, layers[predecessor]))
+
+
+# todo: remove the 'layers' parameter
+def _find_dependencies_filters(sgraph, layers, layer_name, dependencies_list):
+ # Find all instances of Convolution or FC (GEMM) layers that immediately follow this layer
+ successors = sgraph.successors_f(layer_name, ['Conv', 'Gemm'])
+ for successor in successors:
+ dependencies_list.append(successor)
+ next = sgraph.find_op(successor)
+ if next['type'] == 'Conv':
+ if next['attrs']['group'] == next['attrs']['n_ifm']:
+ # This is a group-wise convolution, and a special one at that (groups == in_channels).
+ _find_dependencies_filters(sgraph, layers, successor, dependencies_list)
+ elif next['attrs']['group'] != 1:
+ raise ValueError("Distiller AutoCompression currently does not handle this conv.groups configuration")
+
\ No newline at end of file
diff --git a/examples/auto_compression/amc/utils/features_collector.py b/examples/auto_compression/amc/utils/features_collector.py
new file mode 100755
index 0000000..38bf612
--- /dev/null
+++ b/examples/auto_compression/amc/utils/features_collector.py
@@ -0,0 +1,80 @@
+#
+# Copyright (c) 2018 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import logging
+import numpy as np
+import torch
+from torch.nn import functional as f
+import distiller
+
+
+msglogger = logging.getLogger()
+
+
+def basic_featuremaps_caching_fwd_hook(module, input, output, intermediate_fms):
+ """A trivial function to cache input/output feature-maps
+
+ The input feature-maps are appended to a list of input-maps that are input to
+ this module. This list is provided by an external context. A similar setup
+ exists for output feature-maps.
+
+ This function is invoked from the forward-hook of modules and can be called from
+ various threads and the modules can exist on multiple GPUs. Therefore, we use Python
+ lists (on the CPU) to protect against race-conditions and synchronize the data.
+ Using the CPU to store the lists also benefits from the larger CPU DRAM.
+ """
+ intermediate_fms['output_fms'][module.distiller_name].append(output)
+ intermediate_fms['input_fms'][module.distiller_name].append(input[0])
+
+
+def collect_intermediate_featuremap_samples(model, forward_fn, module_filter_fn,
+ fm_caching_fwd_hook=basic_featuremaps_caching_fwd_hook):
+ """Collect pairs of input/output feature-maps.
+ """
+ from functools import partial
+
+ def install_io_collectors(m, intermediate_fms):
+ if module_filter_fn(m):
+ intermediate_fms['output_fms'][m.distiller_name] = []
+ intermediate_fms['input_fms'][m.distiller_name] = []
+ hook_handles.append(m.register_forward_hook(partial(fm_caching_fwd_hook,
+ intermediate_fms=intermediate_fms)))
+
+ # Register to the forward hooks, then run the forward-pass and collect the data
+ msglogger.warning("==> Collecting input/ouptput feature-map pairs")
+ distiller.assign_layer_fq_names(model)
+ hook_handles = []
+ intermediate_fms = {"output_fms": dict(), "input_fms": dict()}
+ model.apply(partial(install_io_collectors, intermediate_fms=intermediate_fms))
+
+ forward_fn()
+
+ # Unregister from the forward hooks
+ for handle in hook_handles:
+ handle.remove()
+
+ # We now need to concatenate the list of feature-maps to torch tensors.
+ msglogger.info("Concatenating FMs...")
+ model.intermediate_fms = {"output_fms": dict(), "input_fms": dict()}
+ outputs = model.intermediate_fms['output_fms']
+ inputs = model.intermediate_fms['input_fms']
+
+ for (layer_name, X), Y in zip(intermediate_fms['input_fms'].items(), intermediate_fms['output_fms'].values()):
+ inputs[layer_name] = torch.cat(X, dim=0)
+ outputs[layer_name] = torch.cat(Y, dim=0)
+
+ msglogger.warning("<== Done.")
+ del intermediate_fms
\ No newline at end of file
diff --git a/examples/automated_deep_compression/plain20_cifar_baseline_training.yaml b/examples/auto_compression/plain20_cifar_baseline_training.yaml
similarity index 100%
rename from examples/automated_deep_compression/plain20_cifar_baseline_training.yaml
rename to examples/auto_compression/plain20_cifar_baseline_training.yaml
diff --git a/examples/auto_compression/plain20_fine_tune.yaml b/examples/auto_compression/plain20_fine_tune.yaml
new file mode 100755
index 0000000..f618512
--- /dev/null
+++ b/examples/auto_compression/plain20_fine_tune.yaml
@@ -0,0 +1,23 @@
+lr_schedulers:
+ training_lr:
+ class: StepLR # ReduceLROnPlateau
+ step_size: 20
+ #gamma: 0.3 #0.4
+
+ #class: ReduceLROnPlateau
+ #min_lr: 0.00001
+ #patience: 2
+ #factor: 0.7
+
+ #class: CosineAnnealingWarmRestarts
+ #T_0: 30
+
+ #class: CosineAnnealingLR
+ #T_max: 120
+
+policies:
+ - lr_scheduler:
+ instance_name: training_lr
+ starting_epoch: 0
+ ending_epoch: 100
+ frequency: 1
diff --git a/examples/automated_deep_compression/vgg16_cifar_baseline_training.yaml b/examples/auto_compression/vgg16_cifar_baseline_training.yaml
similarity index 100%
rename from examples/automated_deep_compression/vgg16_cifar_baseline_training.yaml
rename to examples/auto_compression/vgg16_cifar_baseline_training.yaml
diff --git a/examples/automated_deep_compression/ADC.py b/examples/automated_deep_compression/ADC.py
deleted file mode 100755
index 80bca3a..0000000
--- a/examples/automated_deep_compression/ADC.py
+++ /dev/null
@@ -1,913 +0,0 @@
-#
-# Copyright (c) 2018 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-"""To execute this code:
-
-$ time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar10 --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc --amc-protocol=mac-constrained --amc-target-density=0.5 -p=50
-
-
-Coach installation:
-===================
-After creating the virtual environment and installing Distiller's Python package dependencies, go ahead and
-setup Coach per: https://github.com/NervanaSystems/coach#installation.
-
-Make sure that you install Coach's package dependencies into the same virtual environment that already contains
-Distiller's dependency packages. You do this by ensuring that Distiller's virtual environment is the active environment
-when you install Coach.
-*NOTE: you may need to update TensorFlow to the expected version:
- $ pip3 install tensorflow==1.9.0
-
-Finally, if you are running Coach in a development environment, you need to tell the Python runtime where to find
-the Coach code:
-$ export PYTHONPATH=<path-to-coach-code>
-
-
-Spinningup installation:
-========================
-
-Spinup require that we use exactly Python 3.6 so if you are not using this Python version see the instructions here:
- http://ubuntuhandbook.org/index.php/2017/07/install-python-3-6-1-in-ubuntu-16-04-lts/
- $ sudo update-alternatives --config python3
-
-For Python 3.6 you may also need to install a new virtual-env:
- $ sudo apt-get install python3.6-venv
-
-Then create and activate your venv, and populate it with the Distiller packages:
- $ python3 -m venv distiller_env_python3.6
- $ source distiller_env_python3.6/bin/activate
- $ pip3 install -r requirements.txt
-
-You want to install Spinup into this venv. First clone Spinup and then install it into your venv:
- $ cd <spinningup-repo>
- $ sudo apt-get install python3.6-dev
- $ pip3 install -e .
-
-
-https://spinningup.openai.com/en/latest/user/installation.html?highlight=license
-
-"""
-import math
-import os
-import copy
-import logging
-import numpy as np
-import torch
-import csv
-try:
- import gym
-except ImportError as e:
- print("WARNING: to use automated compression you will need to install extra packages")
- print("See instructions in the header of examples/automated_deep_compression/ADC.py")
- raise e
-from gym import spaces
-import distiller
-from collections import OrderedDict, namedtuple
-from types import SimpleNamespace
-from distiller import normalize_module_name, SummaryGraph
-from examples.automated_deep_compression.adc_random_env import random_agent
-
-# Choose which RL library to use: Coach from Intel AI Lab, or Spinup from OpenAI
-#RLLIB = "spinup"
-RLLIB = "coach"
-
-
-msglogger = logging.getLogger()
-Observation = namedtuple('Observation', ['n', 'c', 'h', 'w', 'stride', 'k', 'MACs', 'reduced', 'rest', 'prev_a'])
-LayerDesc = namedtuple('LayerDesc', ['t', 'n', 'c', 'h', 'w', 'stride', 'k', 'MACs', 'reduced', 'rest'])
-LayerDescLen = len(LayerDesc._fields)
-ALMOST_ONE = 0.9999
-
-
-class CSVFile(object):
- def __init__(self, fname, headers):
- """Create the CSV file and write the column names"""
- with open(fname, 'w') as f:
- writer = csv.writer(f)
- writer.writerow(headers)
- self.fname = fname
-
- def add_record(self, fields):
- # We close the file each time to flush on every write, and protect against data-loss on crashes
- with open(self.fname, 'a') as f:
- writer = csv.writer(f)
- writer.writerow(fields)
-
-
-class AMCStatsFile(CSVFile):
- def __init__(self, fname):
- headers = ['episode', 'top1', 'reward', 'total_macs', 'normalized_macs',
- 'normalized_nnz', 'ckpt_name', 'action_history', 'agent_action_history']
- super().__init__(fname, headers)
-
-
-class FineTuneStatsFile(CSVFile):
- def __init__(self, fname):
- headers = ['episode', 'ft_top1_list']
- super().__init__(fname, headers)
-
-
-def is_using_continuous_action_space(agent):
- return agent in ("DDPG", "ClippedPPO-continuous", "Random-policy")
-
-
-if RLLIB == "spinup":
- import tensorflow as tf
- from spinup.algos.ddpg import core
- from .ddpg import ddpg
-
- def ddpg_spinup(env1, env2):
- from spinup.utils.run_utils import setup_logger_kwargs
- exp_name = "Test"
- seed = 0
- # The number and size of the Actor-Critic MLP hidden layers
- layers, hid = 2, 300
- logger_kwargs = setup_logger_kwargs(exp_name) # , seed)
-
- ddpg.ddpg(env=env1, test_env=env2, actor_critic=core.mlp_actor_critic,
- ac_kwargs=dict(hidden_sizes=[hid]*layers, output_activation=tf.sigmoid),
- gamma=1, # discount rate
- seed=seed,
- epochs=400,
- replay_size=2000,
- batch_size=64,
- start_steps=env1.amc_cfg.num_heatup_epochs,
- steps_per_epoch=800 * env1.num_layers(), # every 50 episodes perform 10 episodes of testing
- act_noise=0.5,
- pi_lr=1e-4,
- q_lr=1e-3,
- logger_kwargs=logger_kwargs)
-
-
-if RLLIB == "coach":
- from rl_coach.base_parameters import TaskParameters
- from rl_coach.core_types import EnvironmentSteps
- from rl_coach.schedules import ConstantSchedule, PieceWiseSchedule, ExponentialSchedule
-
-
-def log_amc_config(amc_cfg):
- try:
- msglogger.info('AMC configuration:')
- for k, v in amc_cfg.items():
- msglogger.info("\t{} : {}".format(k, v))
- except TypeError as e:
- pass
-
-
-def count_conv_layer(model):
- """Count the number of Convolution layers exist in this model"""
- conv_cnt = 0
- for module in model.modules():
- if type(module) == torch.nn.Conv2d:
- conv_cnt += 1
- return conv_cnt
-
-
-def mac_constrained_experimental_reward_fn(env, top1, top5, vloss, total_macs):
- """A more intuitive reward for constraining the compute and optimizing the
- accuracy under this constraint.
- """
- macs_normalized = total_macs/env.dense_model_macs
- reward = top1/100
- if macs_normalized > (env.amc_cfg.target_density+0.002):
- reward = -3 - macs_normalized
- else:
- reward += 1
- return reward
-
-
-def harmonic_mean_reward_fn(env, top1, top5, vloss, total_macs):
- """This reward is based on the idea of weighted harmonic mean
-
- Balance compute and accuracy provided a beta value that weighs the two components.
- See: https://en.wikipedia.org/wiki/F1_score
- """
- beta = 1
- #beta = 0.75 # How much to favor accuracy
- macs_normalized = total_macs/env.dense_model_macs
- reward = (1 + beta**2) * top1/100 * macs_normalized / (beta**2 * macs_normalized + top1/100)
- return reward
-
-
-def amc_reward_fn(env, top1, top5, vloss, total_macs):
- """This reward punishes the agent when it produces networks that don't comply with the MACs resource-constraint,
- (the negative reward is in proportion to the network density). Otherwise, the reward is the Top1 accuracy.
- """
- if not env.is_macs_constraint_achieved(total_macs):
- current_density = total_macs / env.dense_model_macs
- reward = env.amc_cfg.target_density - current_density
- else:
- reward = top1/100
- return reward
-
-
-def do_adc_internal(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn):
- dataset = args.dataset
- arch = args.arch
- perform_thinning = True # args.amc_thinning
- num_ft_epochs = args.amc_ft_epochs
- action_range = args.amc_action_range
- np.random.seed()
- conv_cnt = count_conv_layer(model)
-
- msglogger.info("Executing AMC: RL agent - %s RL library - %s", args.amc_agent_algo, RLLIB)
-
- # Create a dictionary of parameters that Coach will handover to DistillerWrapperEnvironment
- # Once it creates it.
- services = distiller.utils.MutableNamedTuple({
- 'validate_fn': validate_fn,
- 'save_checkpoint_fn': save_checkpoint_fn,
- 'train_fn': train_fn})
-
- app_args = distiller.utils.MutableNamedTuple({
- 'dataset': dataset,
- 'arch': arch,
- 'optimizer_data': optimizer_data})
-
- amc_cfg = distiller.utils.MutableNamedTuple({
- 'protocol': args.amc_protocol,
- 'agent_algo': args.amc_agent_algo,
- 'perform_thinning': perform_thinning,
- 'num_ft_epochs': num_ft_epochs,
- 'action_range': action_range,
- 'conv_cnt': conv_cnt,
- 'reward_frequency': args.amc_reward_frequency,
- 'ft_frequency': args.amc_ft_frequency,
- 'pruning_pattern': "filters"}) # "channels"}) #
-
- #net_wrapper = NetworkWrapper(model, app_args, services)
- #return sample_networks(net_wrapper, services)
-
- if args.amc_protocol == "accuracy-guaranteed":
- amc_cfg.target_density = None
- amc_cfg.reward_fn = lambda env, top1, top5, vloss, total_macs: -(1-top1/100) * math.log(total_macs)
- amc_cfg.action_constrain_fn = None
- elif args.amc_protocol == "mac-constrained":
- amc_cfg.target_density = args.amc_target_density
- amc_cfg.reward_fn = lambda env, top1, top5, vloss, total_macs: top1/100 #(90.5 - top1) / 10
- amc_cfg.action_constrain_fn = DistillerWrapperEnvironment.get_action
- elif args.amc_protocol == "mac-constrained-experimental":
- amc_cfg.target_density = args.amc_target_density
- amc_cfg.reward_fn = amc_reward_fn
- amc_cfg.action_constrain_fn = None
- else:
- raise ValueError("{} is not supported currently".format(args.amc_protocol))
-
- steps_per_episode = conv_cnt
- if args.amc_agent_algo == "DDPG":
- amc_cfg.heatup_noise = 0.5
- amc_cfg.initial_training_noise = 0.5
- amc_cfg.training_noise_decay = 0.996 # 0.998
- amc_cfg.num_heatup_epochs = args.amc_heatup_epochs
- amc_cfg.num_training_epochs = args.amc_training_epochs
- training_noise_duration = amc_cfg.num_training_epochs * steps_per_episode
- heatup_duration = amc_cfg.num_heatup_epochs * steps_per_episode
-
- if amc_cfg.agent_algo == "Random-policy":
- return random_agent(DistillerWrapperEnvironment(model, app_args, amc_cfg, services))
-
- if RLLIB == "spinup":
- msglogger.info("AMC: Using spinup")
- env1 = DistillerWrapperEnvironment(model, app_args, amc_cfg, services)
- env2 = DistillerWrapperEnvironment(model, app_args, amc_cfg, services)
- ddpg_spinup(env1, env2)
- else:
- msglogger.info("AMC: Using coach")
-
- # When we import the graph_manager from the ADC_DDPG preset, we implicitly instruct
- # Coach to create and use our DistillerWrapperEnvironment environment.
- # So Distiller calls Coach, which creates the environment, trains the agent, and ends.
- if args.amc_agent_algo == "DDPG":
- from examples.automated_deep_compression.presets.ADC_DDPG import graph_manager, agent_params
- agent_params.exploration.noise_percentage_schedule = PieceWiseSchedule([
- (ConstantSchedule(amc_cfg.heatup_noise), EnvironmentSteps(heatup_duration)),
- (ExponentialSchedule(amc_cfg.initial_training_noise, 0, amc_cfg.training_noise_decay),
- EnvironmentSteps(training_noise_duration))])
- # agent_params.exploration.noise_percentage_schedule = ConstantSchedule(0)
- elif "ClippedPPO" in args.amc_agent_algo:
- from examples.automated_deep_compression.presets.ADC_ClippedPPO import graph_manager, agent_params
-
- # These parameters are passed to the Distiller environment
- graph_manager.env_params.additional_simulator_parameters = {'model': model,
- 'app_args': app_args,
- 'amc_cfg': amc_cfg,
- 'services': services}
-
- coach_logs_dir = os.path.join(msglogger.logdir, 'coach')
- os.mkdir(coach_logs_dir)
- task_parameters = TaskParameters(experiment_path=coach_logs_dir)
- graph_manager.create_graph(task_parameters)
- graph_manager.improve()
-
-
-# This is a temporary hack!
-resnet50_params = ["module.layer1.0.conv1.weight", "module.layer1.0.conv2.weight",
- "module.layer1.1.conv1.weight", "module.layer1.1.conv2.weight",
- "module.layer1.2.conv1.weight", "module.layer1.2.conv2.weight",
- "module.layer2.0.conv1.weight", "module.layer2.0.conv2.weight",
- "module.layer2.1.conv1.weight", "module.layer2.1.conv2.weight",
- "module.layer2.2.conv1.weight", "module.layer2.2.conv2.weight",
- "module.layer2.3.conv1.weight", "module.layer2.3.conv2.weight",
- "module.layer3.0.conv1.weight", "module.layer3.0.conv2.weight",
- "module.layer3.1.conv1.weight", "module.layer3.1.conv2.weight",
- "module.layer3.2.conv1.weight", "module.layer3.2.conv2.weight",
- "module.layer3.3.conv1.weight", "module.layer3.3.conv2.weight",
- "module.layer3.4.conv1.weight", "module.layer3.4.conv2.weight",
- "module.layer3.5.conv1.weight", "module.layer3.5.conv2.weight",
- "module.layer4.0.conv1.weight", "module.layer4.0.conv2.weight",
- "module.layer4.1.conv1.weight", "module.layer4.1.conv2.weight",
- "module.layer4.2.conv1.weight", "module.layer4.2.conv2.weight"]
-
-resnet20_params = ["module.layer1.0.conv1.weight", "module.layer1.1.conv1.weight", "module.layer1.2.conv1.weight",
- "module.layer2.0.conv1.weight", "module.layer2.1.conv1.weight", "module.layer2.2.conv1.weight",
- "module.layer3.0.conv1.weight", "module.layer3.1.conv1.weight", "module.layer3.2.conv1.weight"]
-
-resnet56_params = ["module.layer1.0.conv1.weight", "module.layer1.1.conv1.weight", "module.layer1.2.conv1.weight",
- "module.layer1.3.conv1.weight", "module.layer1.4.conv1.weight", "module.layer1.5.conv1.weight",
- "module.layer1.6.conv1.weight", "module.layer1.7.conv1.weight", "module.layer1.8.conv1.weight",
-
- "module.layer2.0.conv1.weight", "module.layer2.1.conv1.weight", "module.layer2.2.conv1.weight",
- "module.layer2.3.conv1.weight", "module.layer2.4.conv1.weight", "module.layer2.5.conv1.weight",
- "module.layer2.6.conv1.weight", "module.layer2.7.conv1.weight", "module.layer2.8.conv1.weight",
-
- "module.layer3.0.conv1.weight", "module.layer3.1.conv1.weight", "module.layer3.2.conv1.weight",
- "module.layer3.3.conv1.weight", "module.layer3.4.conv1.weight", "module.layer3.5.conv1.weight",
- "module.layer3.6.conv1.weight", "module.layer3.7.conv1.weight", "module.layer3.8.conv1.weight"]
-
-plain20_params = ["module.layer1.0.conv1.weight", "module.layer1.0.conv2.weight",
- "module.layer1.1.conv1.weight", "module.layer1.1.conv2.weight",
- "module.layer1.2.conv1.weight", "module.layer1.2.conv2.weight",
- "module.layer2.0.conv1.weight", "module.layer2.0.conv2.weight",
- "module.layer2.1.conv1.weight", "module.layer2.1.conv2.weight",
- "module.layer2.2.conv1.weight", "module.layer2.2.conv2.weight",
- "module.layer3.0.conv1.weight", "module.layer3.0.conv2.weight",
- "module.layer3.1.conv1.weight", "module.layer3.1.conv2.weight",
- "module.layer3.2.conv1.weight", "module.layer3.2.conv2.weight"]
-
-
-resnet50_layers = [param[:-len(".weight")] for param in resnet50_params]
-resnet20_layers = [param[:-len(".weight")] for param in resnet20_params]
-resnet56_layers = [param[:-len(".weight")] for param in resnet56_params]
-plain20_layers = [param[:-len(".weight")] for param in plain20_params]
-
-
-class NetworkWrapper(object):
- def __init__(self, model, app_args, services):
- self.app_args = app_args
- self.services = services
- self.conv_layers, _, _ = self.collect_conv_details(model)
- self.reset(model)
-
- def get_model_resources_requirements(self, model=None):
- if model is None:
- model = self.model
- _, total_macs, total_nnz = collect_conv_details(model, self.app_args.dataset, True)
- return total_macs, total_nnz
-
- @property
- def arch(self):
- return self.app_args.arch
-
- def collect_conv_details(self, model):
- # Temporary ugly hack!
- resnet_layers = None
- if self.app_args.arch == "resnet20_cifar":
- resnet_layers = resnet20_layers
- elif self.app_args.arch == "resnet56_cifar":
- resnet_layers = resnet56_layers
- elif self.app_args.arch == "resnet50":
- resnet_layers = resnet50_layers
- elif self.app_args.arch == "plain20_cifar":
- resnet_layers = plain20_layers
- return collect_conv_details(model, self.app_args.dataset, True, resnet_layers)
-
- def num_layers(self):
- return len(self.conv_layers)
-
- def get_layer(self, idx):
- try:
- return self.conv_layers[idx]
- except KeyError:
- return None
-
- def get_layer_macs(self, layer):
- """Return the number of MACs required to compute <layer>'s Convolution"""
- if layer is None:
- return 0
- conv_module = distiller.model_find_module(self.model, layer.name)
- # MACs = volume(OFM) * (#IFM * K^2)
- dense_macs = (conv_module.out_channels * layer.ofm_h * layer.ofm_w) * (conv_module.in_channels * layer.k**2)
- return dense_macs
-
- def reset(self, model):
- self.model = model
- self.zeros_mask_dict = distiller.create_model_masks_dict(self.model)
-
- def create_scheduler(self):
- scheduler = distiller.CompressionScheduler(self.model)
- masks = {param_name: masker.mask for param_name, masker in self.zeros_mask_dict.items()}
- scheduler.load_state_dict(state={'masks_dict': masks})
- return scheduler
-
- def remove_structures(self, layer_id, fraction_to_prune, prune_what="channels"):
- """Physically remove channels and corresponding filters from the model
-
- Returns the compute-sparsity of the layer with index 'layer_id'
- """
- if layer_id not in range(self.num_layers()):
- raise ValueError("idx=%d is not in correct range (0-%d)" % (layer_id, self.num_layers()))
- if fraction_to_prune < 0:
- raise ValueError("fraction_to_prune=%f is illegal" % (fraction_to_prune))
-
- if fraction_to_prune == 0:
- return 0
- if fraction_to_prune == 1.0:
- # For now, prevent the removal of entire layers
- fraction_to_prune = ALMOST_ONE
-
- layer = self.conv_layers[layer_id]
- macs_before = self.get_layer_macs(layer)
- conv_pname = layer.name + ".weight"
- conv_p = distiller.model_find_param(self.model, conv_pname)
-
- msglogger.info("ADC: trying to remove %.1f%% %s from %s" % (fraction_to_prune*100, prune_what, conv_pname))
-
- if prune_what == "channels":
- calculate_sparsity = distiller.sparsity_ch
- remove_structures_fn = distiller.remove_channels
- group_type = "Channels"
- elif prune_what == "filters":
- calculate_sparsity = distiller.sparsity_3D
- group_type = "Filters"
- remove_structures_fn = distiller.remove_filters
- else:
- raise ValueError("unsupported structure {}".format(prune_what))
- # Create a channel-ranking pruner
- pruner = distiller.pruning.L1RankedStructureParameterPruner("adc_pruner", group_type,
- fraction_to_prune, conv_pname)
- pruner.set_param_mask(conv_p, conv_pname, self.zeros_mask_dict, meta=None)
- del pruner
-
- if (self.zeros_mask_dict[conv_pname].mask is None or
- calculate_sparsity(self.zeros_mask_dict[conv_pname].mask) == 0):
- msglogger.info("remove_structures: aborting because there are no structures to prune")
- return 0
-
- # Use the mask to prune
- self.zeros_mask_dict[conv_pname].apply_mask(conv_p)
- remove_structures_fn(self.model, self.zeros_mask_dict, self.app_args.arch, self.app_args.dataset, optimizer=None)
- conv_p = distiller.model_find_param(self.model, conv_pname)
- return 1 - (self.get_layer_macs(layer) / macs_before)
-
- def validate(self):
- top1, top5, vloss = self.services.validate_fn(model=self.model)
- return top1, top5, vloss
-
- def train(self, num_epochs, episode=0):
- # Train for zero or more epochs
- opt_cfg = self.app_args.optimizer_data
- optimizer = torch.optim.SGD(self.model.parameters(), lr=opt_cfg['lr'],
- momentum=opt_cfg['momentum'], weight_decay=opt_cfg['weight_decay'])
- compression_scheduler = self.create_scheduler()
- acc_list = []
- for _ in range(num_epochs):
- # Fine-tune the model
- accuracies = self.services.train_fn(model=self.model, compression_scheduler=compression_scheduler,
- optimizer=optimizer, epoch=episode)
- acc_list.extend(accuracies)
- del compression_scheduler
- return acc_list
-
-
-class DistillerWrapperEnvironment(gym.Env):
- def __init__(self, model, app_args, amc_cfg, services):
- self.pylogger = distiller.data_loggers.PythonLogger(msglogger)
- self.tflogger = distiller.data_loggers.TensorBoardLogger(msglogger.logdir)
- self.orig_model = model
- self.app_args = app_args
- self.amc_cfg = amc_cfg
- self.services = services
- self.net_wrapper = NetworkWrapper(model, app_args, services)
- self.dense_model_macs, self.dense_model_size = self.net_wrapper.get_model_resources_requirements(model)
-
- self.reset(init_only=True)
- msglogger.info("Model %s has %d Convolution layers", self.app_args.arch, self.net_wrapper.num_layers())
- msglogger.info("\tTotal MACs: %s" % distiller.pretty_int(self.dense_model_macs))
- log_amc_config(amc_cfg)
-
- self.episode = 0
- self.best_reward = -1000
- self.action_low = amc_cfg.action_range[0]
- self.action_high = amc_cfg.action_range[1]
- # Gym spaces documentation: https://gym.openai.com/docs/
- if is_using_continuous_action_space(self.amc_cfg.agent_algo):
- self.action_space = spaces.Box(self.action_low, self.action_high, shape=(1,))
- self.action_space.default_action = self.action_low
- else:
- self.action_space = spaces.Discrete(10)
- self.STATE_EMBEDDING_LEN = len(Observation._fields)
- #self.observation_space = spaces.Box(0, float("inf"), shape=(self.STATE_EMBEDDING_LEN+self.num_layers(),))
- self.observation_space = spaces.Box(0, float("inf"), shape=(self.STATE_EMBEDDING_LEN+1,))
- #self.observation_space = spaces.Box(0, float("inf"), shape=(LayerDescLen * self.num_layers(), ))
- #self.create_network_record_file()
- self.stats_file = AMCStatsFile(os.path.join(msglogger.logdir, 'amc.csv'))
- self.ft_stats_file = FineTuneStatsFile(os.path.join(msglogger.logdir, 'ft_top1.csv'))
-
- def reset(self, init_only=False):
- """Reset the environment.
- This is invoked by the Agent.
- """
- msglogger.info("Resetting the environment (init_only={})".format(init_only))
- self.current_layer_id = 0
- self.prev_action = 0
- self.model = copy.deepcopy(self.orig_model)
- self.net_wrapper.reset(self.model)
- self._removed_macs = 0
- self.action_history = []
- self.agent_action_history = []
- if init_only:
- return
- initial_observation = self.get_obs()
- return initial_observation
-
- def current_layer(self):
- return self.net_wrapper.get_layer(self.current_layer_id)
-
- def episode_is_done(self):
- return self.current_layer_id == self.net_wrapper.num_layers()
-
- def removed_macs(self):
- """Return the amount of MACs removed so far.
- This is normalized to the range 0..1
- """
- return self._removed_macs / self.dense_model_macs
-
- def render(self, mode='human'):
- """Provide some feedback to the user about what's going on.
- This is invoked by the Agent.
- """
- if self.current_layer_id == 0:
- msglogger.info("+" + "-" * 50 + "+")
- msglogger.info("Starting a new episode %d", self.episode)
- msglogger.info("+" + "-" * 50 + "+")
-
- msglogger.info("Render Environment: current_layer_id=%d" % self.current_layer_id)
- distiller.log_weights_sparsity(self.model, -1, loggers=[self.pylogger])
-
- def get_action(self, pruning_action):
- """Compute a resource-constrained action"""
- reduced = self._removed_macs
- rest = self.rest_macs_raw() * self.action_high
- target_reduction = (1 - self.amc_cfg.target_density) * self.dense_model_macs
-
- duty = target_reduction - (reduced + rest)
- flops = self.net_wrapper.get_layer_macs(self.current_layer())
- assert flops > 0
- pruning_action_final = min(self.action_high, max(pruning_action, duty/flops))
- if pruning_action_final != pruning_action:
- msglogger.info("action ********** pruning_action={}==>pruning_action_final={:.2f}: reduced={:.2f} rest={:.2f} target={:.2f} duty={:.2f} flops={:.2f}".
- format(pruning_action, pruning_action_final, reduced/self.dense_model_macs,
- rest/self.dense_model_macs, 1-self.amc_cfg.target_density,
- duty/self.dense_model_macs,
- flops/self.dense_model_macs))
- return pruning_action_final
-
- def step(self, pruning_action):
- """Take a step, given an action.
-
- The action represents the desired sparsity for the "current" layer.
- This function is invoked by the Agent.
- """
- msglogger.info("env.step - current_layer_id={} episode={}".format(self.current_layer_id, self.episode))
- pruning_action = pruning_action[0]
- msglogger.info("\tAgent pruning_action={}".format(pruning_action))
- self.agent_action_history.append(pruning_action)
-
- if is_using_continuous_action_space(self.amc_cfg.agent_algo):
- pruning_action = np.clip(pruning_action, self.action_low, self.action_high)
- else:
- # Divide the action space into 10 discrete levels (0%, 10%, 20%,....90% sparsity)
- pruning_action = pruning_action / 10
- msglogger.info("\tAgent clipped pruning_action={}".format(pruning_action))
-
- if self.amc_cfg.action_constrain_fn is not None:
- pruning_action = self.amc_cfg.action_constrain_fn(self, pruning_action=pruning_action)
- msglogger.info("Constrained pruning_action={}".format(pruning_action))
-
- # Calculate the final compression rate
- total_macs_before, _ = self.net_wrapper.get_model_resources_requirements(self.model)
- layer_macs = self.net_wrapper.get_layer_macs(self.current_layer())
- msglogger.info("\tlayer_macs={:.2f}".format(layer_macs / self.dense_model_macs))
- msglogger.info("\tremoved_macs={:.2f}".format(self.removed_macs()))
- msglogger.info("\trest_macs={:.2f}".format(self.rest_macs()))
-
- if pruning_action > 0:
- pruning_action = self.net_wrapper.remove_structures(self.current_layer_id,
- fraction_to_prune=pruning_action,
- prune_what=self.amc_cfg.pruning_pattern)
- else:
- pruning_action = 0
-
- self.action_history.append(pruning_action)
- total_macs_after, _ = self.net_wrapper.get_model_resources_requirements(self.model)
- layer_macs_after_action = self.net_wrapper.get_layer_macs(self.current_layer())
-
- # Update the various counters after taking the step
- self.current_layer_id += 1
- self._removed_macs += (total_macs_before - total_macs_after)
-
- msglogger.info("actual_action={}".format(pruning_action))
- msglogger.info("layer_macs={} layer_macs_after_action={} removed now={}".format(layer_macs,
- layer_macs_after_action,
- (layer_macs - layer_macs_after_action)))
- msglogger.info("self._removed_macs={}".format(self._removed_macs))
- assert math.isclose(layer_macs_after_action / layer_macs, 1 - pruning_action)
-
- stats = ('Performance/Validation/',
- OrderedDict([('requested_action', pruning_action)]))
-
- distiller.log_training_progress(stats, None,
- self.episode, steps_completed=self.current_layer_id,
- total_steps=self.amc_cfg.conv_cnt, log_freq=1, loggers=[self.tflogger])
-
- if self.episode_is_done():
- msglogger.info("Episode is ending")
- observation = self.get_final_obs()
- reward, top1, total_macs, total_nnz = self.compute_reward()
- normalized_macs = total_macs / self.dense_model_macs * 100
- normalized_nnz = total_nnz / self.dense_model_size * 100
- self.finalize_episode(top1, reward, total_macs, normalized_macs,
- normalized_nnz, self.action_history, self.agent_action_history)
- self.episode += 1
- else:
- if self.amc_cfg.ft_frequency is not None and self.current_layer_id % self.amc_cfg.ft_frequency == 0:
- self.net_wrapper.train(1, self.episode)
- observation = self.get_obs()
- if self.amc_cfg.reward_frequency is not None and self.current_layer_id % self.amc_cfg.reward_frequency == 0:
- reward, top1, total_macs, total_nnz = self.compute_reward(False)
- else:
- reward = 0
- self.prev_action = pruning_action
- info = {}
- return observation, reward, self.episode_is_done(), info
-
- def one_hot(self, n, r):
- """Produce a one-hot representation of the layer id"""
- #return [1 if i == n else 0 for i in range(r)]
- return [n]
-
- def get_obs(self):
- """Produce a state embedding (i.e. an observation)"""
-
- current_layer_macs = self.net_wrapper.get_layer_macs(self.current_layer())
- current_layer_macs_pct = current_layer_macs/self.dense_model_macs
- current_layer = self.current_layer()
- conv_module = distiller.model_find_module(self.model, current_layer.name)
- obs = [#current_layer.t,
- conv_module.out_channels,
- conv_module.in_channels,
- current_layer.ifm_h,
- current_layer.ifm_w,
- current_layer.stride[0],
- current_layer.k,
- current_layer_macs_pct*100,
- self.removed_macs()*100,
- self.rest_macs()*100,
- self.prev_action*100]
- onehot_id = self.one_hot(self.current_layer_id, self.net_wrapper.num_layers())
- msglogger.info("obs={} {}".format(onehot_id, Observation._make(obs)))
- obs = np.array(onehot_id + obs)
- assert (self.removed_macs() + current_layer_macs_pct + self.rest_macs()) <= 1
- return obs
-
- def get_final_obs(self):
- """Return the final state embedding (observation)
- The final state is reached after we traverse all of the Convolution layers.
- """
- obs = [#-1,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- self.removed_macs()*100,
- self.rest_macs()*100,
- self.prev_action*100]
- onehot_id = self.one_hot(self.net_wrapper.num_layers(), self.net_wrapper.num_layers())
- msglogger.info("obs={} {}".format(onehot_id, Observation._make(obs)))
- obs = np.array(onehot_id + obs)
- return obs
-
- def whole_network_get_obs(self):
- """Produce a state embedding (i.e. an observation)"""
- num_layers = self.net_wrapper.num_layers()
- network_obs = np.empty(shape=(LayerDescLen, num_layers))
- for layer_id in range(num_layers):
- layer = self.get_layer(layer_id)
- layer_macs = self.net_wrapper.get_layer_macs(layer)
- layer_macs_pct = layer_macs/self.dense_model_macs
- conv_module = distiller.model_find_module(self.model, layer.name)
- obs = [layer.t,
- conv_module.out_channels,
- conv_module.in_channels,
- layer.ifm_h,
- layer.ifm_w,
- layer.stride[0],
- layer.k,
- layer_macs_pct,
- self.removed_macs(),
- self.rest_macs()]
- network_obs[:, layer_id] = np.array(obs)
-
- #msglogger.info("obs={} {}".format(onehot_id, Observation._make(obs)))
- #network_obs = network_obs.reshape(network_obs.shape[0], network_obs.shape[1], 1)
- network_obs = network_obs.reshape(network_obs.shape[0] * network_obs.shape[1])
- #msglogger.info("* obs={}".format(network_obs))
- return network_obs
-
- def whole_network_get_final_obs(self):
- return self.get_obs()
-
- def rest_macs_raw(self):
- """Return the number of remaining MACs in the layers following the current layer"""
- rest = 0
- for layer_id in range(self.current_layer_id, self.net_wrapper.num_layers()):
- rest += self.net_wrapper.get_layer_macs(self.net_wrapper.get_layer(layer_id + 1))
- return rest
-
- def rest_macs(self):
- return self.rest_macs_raw() / self.dense_model_macs
-
- def is_macs_constraint_achieved(self, compressed_model_total_macs):
- current_density = compressed_model_total_macs / self.dense_model_macs
- return self.amc_cfg.target_density >= current_density
-
- def compute_reward(self, log_stats=True):
- """Compute the reward"""
- distiller.log_weights_sparsity(self.model, -1, loggers=[self.pylogger])
- total_macs, total_nnz = self.net_wrapper.get_model_resources_requirements(self.model)
- if self.amc_cfg.perform_thinning:
- compression = distiller.model_numel(self.model, param_dims=[4]) / self.dense_model_size
- else:
- compression = 1 - distiller.model_sparsity(self.model)/100
- # What a hack!
- total_nnz *= compression
-
- accuracies = self.net_wrapper.train(self.amc_cfg.num_ft_epochs, self.episode)
- self.ft_stats_file.add_record([self.episode, accuracies])
-
- top1, top5, vloss = self.net_wrapper.validate()
- reward = self.amc_cfg.reward_fn(self, top1, top5, vloss, total_macs)
-
- if log_stats:
- macs_normalized = total_macs/self.dense_model_macs
- msglogger.info("Total parameters left: %.2f%%" % (compression*100))
- msglogger.info("Total compute left: %.2f%%" % (total_macs/self.dense_model_macs*100))
-
- stats = ('Performance/EpisodeEnd/',
- OrderedDict([('Loss', vloss),
- ('Top1', top1),
- ('Top5', top5),
- ('reward', reward),
- ('total_macs', int(total_macs)),
- ('macs_normalized', macs_normalized*100),
- ('log(total_macs)', math.log(total_macs)),
- ('total_nnz', int(total_nnz))]))
- distiller.log_training_progress(stats, None, self.episode, steps_completed=0, total_steps=1,
- log_freq=1, loggers=[self.tflogger, self.pylogger])
- return reward, top1, total_macs, total_nnz
-
- def finalize_episode(self, top1, reward, total_macs, normalized_macs,
- normalized_nnz, action_history, agent_action_history):
- """Write the details of one network to a CSV file and create a checkpoint file"""
- if reward > self.best_reward:
- self.best_reward = reward
- ckpt_name = self.save_checkpoint(is_best=True)
- msglogger.info("Best reward={} episode={} top1={}".format(reward, self.episode, top1))
- else:
- ckpt_name = self.save_checkpoint(is_best=False)
-
- fields = [self.episode, top1, reward, total_macs, normalized_macs,
- normalized_nnz, ckpt_name, action_history, agent_action_history]
- self.stats_file.add_record(fields)
-
- def save_checkpoint(self, is_best=False):
- """Save the learned-model checkpoint"""
- scheduler = self.net_wrapper.create_scheduler()
- episode = str(self.episode).zfill(3)
- if is_best:
- fname = "BEST_adc_episode_{}".format(episode)
- else:
- fname = "adc_episode_{}".format(episode)
-
- self.services.save_checkpoint_fn(epoch=0, model=self.model,
- scheduler=scheduler, name=fname)
- del scheduler
- return fname
-
-
-def collect_conv_details(model, dataset, perform_thinning, layers_to_prune=None):
- dummy_input = distiller.get_dummy_input(dataset)
- g = SummaryGraph(model, dummy_input)
- conv_layers = OrderedDict()
- total_macs = 0
- total_params = 0
- for id, (name, m) in enumerate(model.named_modules()):
- if isinstance(m, torch.nn.Conv2d):
- conv = SimpleNamespace()
- conv.t = len(conv_layers)
- conv.k = m.kernel_size[0]
- conv.stride = m.stride
-
- # Use the SummaryGraph to obtain some other details of the models
- conv_op = g.find_op(normalize_module_name(name))
- assert conv_op is not None
-
- conv.weights_vol = conv_op['attrs']['weights_vol']
- total_params += conv.weights_vol
- conv.macs = conv_op['attrs']['MACs']
- conv_pname = name + ".weight"
- conv_p = distiller.model_find_param(model, conv_pname)
- if not perform_thinning:
- #conv.macs *= distiller.density_ch(conv_p) # Channel pruning
- conv.macs *= distiller.density_3D(conv_p) # Filter pruning
- total_macs += conv.macs
-
- conv.ofm_h = g.param_shape(conv_op['outputs'][0])[2]
- conv.ofm_w = g.param_shape(conv_op['outputs'][0])[3]
- conv.ifm_h = g.param_shape(conv_op['inputs'][0])[2]
- conv.ifm_w = g.param_shape(conv_op['inputs'][0])[3]
-
- conv.name = name
- conv.id = id
- if layers_to_prune is None or name in layers_to_prune:
- conv_layers[len(conv_layers)] = conv
- return conv_layers, total_macs, total_params
-
-
-import pandas as pd
-def sample_networks(net_wrapper, services):
- """Sample networks from the posterior distribution.
-
- 1. Sort the networks we discovered using AMC by their reward.
- 2. Use the top 10% best-performing networks discovered by AMC to postulate a posterior distribution of the
- density/sparsity of each layer:
- p([layers-sparsity] | Top1, L1)
- 3. Sample 100 networks from this distribution.
- For each such network: fine-tune, score using Top1, and save
- """
- #fname = "logs/resnet20___2019.01.29-102912/amc.csv"
- fname = "logs/resnet20___2019.02.03-210001/amc.csv"
- df = pd.read_csv(fname)
-
- #top1_sorted_df = df.sort_values(by=['top1'], ascending=False)
- top1_sorted_df = df.sort_values(by=['reward'], ascending=False)
- top10pct = top1_sorted_df[:int(len(df.index) * 0.1)]
-
- dense_macs, _ = net_wrapper.get_model_resources_requirements()
- layer_sparsities_list = []
- for index, row in top10pct.iterrows():
- layer_sparsities = row['action_history']
- layer_sparsities = layer_sparsities[1:-1].split(",") # convert from string to list
- layer_sparsities = [float(sparsity) for sparsity in layer_sparsities]
- layer_sparsities_list.append(layer_sparsities)
-
- layer_sparsities = np.array(layer_sparsities_list)
- mean = layer_sparsities.mean(axis=0)
- cov = np.cov(layer_sparsities.T)
- num_networks = 100
- data = np.random.multivariate_normal(mean, cov, num_networks)
-
- orig_model = net_wrapper.model
- for i in range(num_networks):
- model = copy.deepcopy(orig_model)
- net_wrapper.reset(model)
- for layer_id, sparsity_level in enumerate(data[i]):
- sparsity_level = min(max(0, sparsity_level), ALMOST_ONE)
- net_wrapper.remove_structures(layer_id,
- fraction_to_prune=sparsity_level,
- prune_what="channels")
-
- net_wrapper.train(1)
- top1, top5, vloss = net_wrapper.validate()
-
- """Save the learned-model checkpoint"""
- scheduler = net_wrapper.create_scheduler()
- total_macs, _ = net_wrapper.get_model_resources_requirements(model)
- fname = "{}_top1_{:2f}__density_{:2f}_sampled".format(net_wrapper.arch, top1, total_macs/dense_macs)
- services.save_checkpoint_fn(epoch=0, model=net_wrapper.model,
- scheduler=scheduler, name=fname)
- del scheduler
diff --git a/examples/automated_deep_compression/__init__.py b/examples/automated_deep_compression/__init__.py
deleted file mode 100755
index 5b2e97c..0000000
--- a/examples/automated_deep_compression/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .automl_args import add_automl_args
-
-def do_adc(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn):
- from .ADC import do_adc_internal
- do_adc_internal(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)
diff --git a/examples/automated_deep_compression/adc_random_env.py b/examples/automated_deep_compression/adc_random_env.py
deleted file mode 100755
index 127f9ee..0000000
--- a/examples/automated_deep_compression/adc_random_env.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import random
-import numpy as np
-from scipy.stats import truncnorm
-import logging
-
-msglogger = logging.getLogger()
-
-
-class RandomADCActionSpace(object):
- def __init__(self, low, high, std, mean, num_layers):
- self.action_low = low
- self.action_high = high
- self.layer = 0
- self.num_layers = num_layers
- self.means = [mean] * num_layers
- #self.means = [high-low] * self.num_layers
- #self.means = [0.9, 0.9, 0.9, 0.9, 0.9, 0.8, 0.8, 0.7, 0.7, 0.6, 0.6, 0.5, 0.5]
- self.std = std
-
- def sample(self):
- return random.uniform(self.action_low, self.action_high)
- # action_values_mean = self.means[self.layer]
- # action_values_std = self.std
- # normalized_low = (self.action_low - action_values_mean) / action_values_std
- # normalized_high = (self.action_high - action_values_mean) / action_values_std
- # distribution = truncnorm(normalized_low, normalized_high, loc=action_values_mean, scale=action_values_std)
- # action = distribution.rvs(1)
- # # action = np.random.normal(self.means[self.layer], self.std)
- # # action = min(self.action_high, max(action, self.action_low))
- # self.layer = (self.layer + 1) % self.num_layers
- # return action
-
- def set_cfg(self, means, std):
- self.means = [0.01*m for m in self.means] + [0.99*m for m in means]
- self.std = std
-
-
-def random_agent(env):
- """Random ADC agent"""
- action_range = env.amc_cfg.action_range
- best_episode = [-1000, None]
- update_rate = 5
- num_layers = env.net_wrapper.num_layers()
- env.action_space = RandomADCActionSpace(action_range[0], action_range[1],
- std=0.35, mean=env.amc_cfg.target_density, num_layers=num_layers)
- for ep in range(1000):
- observation = env.reset()
- action_config = []
- for t in range(100):
- #env.render(0, 0)
- msglogger.info("[episode={}:{}] observation = {}".format(ep, t, observation))
- # take a random action
- action = env.action_space.sample()
- action_config.append(action)
- observation, reward, done, info = env.step([action])
- if done:
- msglogger.info("Episode finished after {} timesteps".format(t+1))
- msglogger.info("^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^")
- msglogger.info("New solution found: episode={} reward={} config={}".format(ep, reward, action_config))
- break
- if reward > best_episode[0]:
- best_episode[0] = reward
- best_episode[1] = action_config
- if ep % update_rate == 0:
- env.action_space.set_cfg(means=best_episode[1], std=0.4)
- best_episode = [-1000, None]
diff --git a/examples/automated_deep_compression/amc-results.ipynb b/examples/automated_deep_compression/amc-results.ipynb
deleted file mode 100644
index 9ff1e16..0000000
--- a/examples/automated_deep_compression/amc-results.ipynb
+++ /dev/null
@@ -1,838 +0,0 @@
-{
- "cells": [
- {
- "attachments": {
- "amc_figure2.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# AutoML for Model Compression\n",
- "\n",
- "This notebook will help visualize and review the results of recreating the results of [AMC](https://arxiv.org/abs/1802.03494).\n",
- "\n",
- "\n",
- "AMC trains a [Deep Deterministic Policy Gradient](https://spinningup.openai.com/en/latest/algorithms/ddpg.html) (DDPG) RL agent to compress DNNs. For the implementation of DDPG I'm using [Coach](https://github.com/NervanaSystems/coach), which is developed and maintained by a team of my colleagues at Intel AI Lab. Coach is arguably the richest open-source RL framework currently available for RL research, with loads of implemented algorithms and supported RL environments. We wrap Distiller in an OpenAI Gym environment API (https://github.com/openai/gym) so that it presents a new environment to Coach. We execute Distiller's sample application ```compress_classifier.py``` with special arguments telling it how to invoke AMC. This creates a ```DistillerWrapperEnvironment``` environment and a DDPG agent and starts the training cycle. For the exact technical details of doing this yourself, see the documentation at the top of ```examples/automated_deep_compression/ADC.py``` (note that in some cases the code uses ADC - [Automated Deep Compression](https://www.researchgate.net/publication/323141651_ADC_Automated_Deep_Compression_and_Acceleration_with_Reinforcement_Learning) - the name of the first version of the AMC paper).\n",
- "\n",
- "This notebook only reviews Resource-Constrained Compression. Specifically, I want to recreate the results of the first row of Table 2: using AMC $R_{err}$ on Plain20-CIFAR with a 50% FLOPs constraint. I pasted Table 2 below with the experiment we are recreating highlighted. \n",
- "\n",
- "\n",
- "Plain20 is defined in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) and it is essentially ResNet20-CIFAR without the residual connections. You will need to train this network first and you can do that using ```examples/automated_deep_compression/plain20_cifar_baseline_training.yaml```. The trained Plain20 achieves Top1 accuracy of 90.5% on CIFAR-10.\n",
- "The notebook uses data captured in Distiller AMC trace files.\n",
- "\n",
- "## TLDR\n",
- "\n",
- "It doesn't work for me yet. The agent seems to learn one action: minimize sparsity (i.e. prune as little as possible). \n",
- "\n",
- "Your help is welcome!"
- ]
- },
- {
- "attachments": {
- "adc_figure2.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Experiment setup\n",
- "\n",
- "Let's let AMC run the full-course: about 635 episodes. We *heat up* for the first 100 episodes. During this phase we don't train: the **Actor** emits actions, to which we add a strong constant noise and fill the Replay Buffer (the AMC paper refers to this phase as the *Exploration Phase*).\n",
- "After completing the **heatup phase** (100 episodes) we start the learning phase (in AMC parlance this is the *Exploitation Phase*) in which we use an exponentially-decaying noise. \n",
- "\n",
- "How should the learning process look like (e.g. the reward curve)? Unfortunately, the AMC paper provides very little information about this. In the ADC paper we find Diagram 2 which provides some clues (why was this removed from AMC?). Remember that RL learning is stochastic and two executions of the same experiment can look quite different: one succeeding and another failing - so we need to run our experiments several time. I assume that ADC-Diagram 2 is a best-case scenario.\n",
- "\n",
- "\n",
- "\n",
- "### DDPG configuration\n",
- "Actor network: \n",
- " - Two hidden layers, each with 300 units. \n",
- " - Final output layer: sigmoid layer to bound the actions within (0; 1). \n",
- "Critic network (Q):\n",
- " - Two hidden layers, each with 300 units. \n",
- " - Actions were included in the second hidden layer. \n",
- " - 0.01 for the soft target updates. \n",
- "Training:\n",
- " - Batch size = 64\n",
- " - Replay buffer size = 2000. \n",
- " - Exploration noise: truncated normal distribution. Constant 0.5 during exploration; exponential decay during exploitation\n",
- "\n",
- "### Distiller AMC experiments\n",
- "Let's look at three executions of 50% FLOPs-Constrained Compression of Plain20:\n",
- "```\n",
- "$ python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar --resume=plain20.checkpoint.pth.tar --lr=0.05 --amc --amc-action-range 0.05 0.80 --vs=0.8\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Notebook code\n",
- "\n",
- "Skip this part - it is necessary only for creating the diagrams. You may also toggle the code-view button."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<script>\n",
- "code_show=true; \n",
- "function code_toggle() {\n",
- " if (code_show){\n",
- " $('div.input').hide();\n",
- " } else {\n",
- " $('div.input').show();\n",
- " }\n",
- " code_show = !code_show\n",
- "} \n",
- "$( document ).ready(code_toggle);\n",
- "</script>\n",
- "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off code view\"></form>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from IPython.display import HTML\n",
- "\n",
- "HTML('''<script>\n",
- "code_show=true; \n",
- "function code_toggle() {\n",
- " if (code_show){\n",
- " $('div.input').hide();\n",
- " } else {\n",
- " $('div.input').show();\n",
- " }\n",
- " code_show = !code_show\n",
- "} \n",
- "$( document ).ready(code_toggle);\n",
- "</script>\n",
- "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off code view\"></form>''')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "\n",
- "import numpy as np\n",
- "import pandas as pd \n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib \n",
- "import csv\n",
- "from matplotlib.ticker import FuncFormatter\n",
- "import ipywidgets as widgets\n",
- "from ipywidgets import interactive, interact, Layout\n",
- "import matplotlib.pylab as pylab\n",
- "import matplotlib.animation as animation\n",
- "from matplotlib import animation, rc\n",
- "\n",
- "\n",
- "\n",
- "#plt.style.use('seaborn') # pretty matplotlib plots\n",
- "\n",
- "params = {'legend.fontsize': 'x-large',\n",
- " 'figure.figsize': (15, 7),\n",
- " 'axes.labelsize': 'x-large',\n",
- " 'axes.titlesize':'xx-large',\n",
- " 'xtick.labelsize':'x-large',\n",
- " 'ytick.labelsize':'x-large'}\n",
- "pylab.rcParams.update(params)\n",
- "\n",
- "\n",
- "def to_percent(y, position):\n",
- " # Ignore the passed in position. This has the effect of scaling the default\n",
- " # tick locations.\n",
- " if y < 1:\n",
- " y = str(100 * y)\n",
- " s = str(y)\n",
- "\n",
- " # The percent symbol needs escaping in latex\n",
- " if matplotlib.rcParams['text.usetex'] is True:\n",
- " return s + r'$\\%$'\n",
- " else:\n",
- " return s + '%'\n",
- " \n",
- "# Widen the cells to get entire rows in the screen.\n",
- "from IPython.core.display import display, HTML\n",
- "#display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
- "\n",
- "\n",
- "def plot_layer_densities(df, idx, action_type='action_history', ax=None, color=None):\n",
- " if ax is None:\n",
- " plt.figure()\n",
- " ax = plt\n",
- " \n",
- " record = df.iloc[idx]\n",
- " layer_sparsities = record[action_type]\n",
- " layer_sparsities = layer_sparsities[1:-1].split(\",\")\n",
- " layer_densities = [1.- float(sparsity) for sparsity in layer_sparsities]\n",
- " ax.bar(range(len(layer_densities)), layer_densities, color=color)\n",
- " ax.set_title(\"Ep:{} - Top1:{:.1f}%\\nMACs:{:.1f}%\".format(record['episode'], \n",
- " record['top1'], \n",
- " record['normalized_macs']))\n",
- " \n",
- " \n",
- "def smooth(data, win_size):\n",
- " win_size = max(1, win_size)\n",
- " return [np.mean(data[max(0, i-win_size):i]) for i in range(len(data))]\n",
- "\n",
- "\n",
- "def plot_performance(alpha, window_size, top1, macs, params, reward):\n",
- " plot_kwargs = {\"figsize\":(15,7), \"lw\": 1, \"alpha\": alpha, \"title\": \"Performance Data\"}\n",
- " smooth_kwargs = {\"lw\": 2, \"legend\": True}\n",
- " if top1:\n",
- " ax = df['top1'].plot(**plot_kwargs, color=\"b\", grid=True)\n",
- " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
- " df['smooth_top1'] = smooth(df['top1'], window_size)\n",
- " df['smooth_top1'].plot(**smooth_kwargs, color=\"b\")\n",
- " if macs:\n",
- " ax = df['normalized_macs'].plot(**plot_kwargs, color=\"r\")\n",
- " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
- " df['smooth_normalized_macs'] = smooth(df['normalized_macs'], window_size)\n",
- " df['smooth_normalized_macs'].plot(**smooth_kwargs, color=\"r\")\n",
- " if params:\n",
- " ax = df['normalized_nnz'].plot(**plot_kwargs, color=\"black\")\n",
- " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
- " df['smooth_normalized_nnz'] = smooth(df['normalized_nnz'], window_size)\n",
- " df['smooth_normalized_nnz'].plot(**smooth_kwargs, color=\"black\") \n",
- " if reward:\n",
- " ax = df['reward'].plot(**plot_kwargs, secondary_y=True, color=\"g\")\n",
- " ax.set(xlabel=\"Episode\", ylabel=\"reward\")\n",
- " df['smooth_reward'] = smooth(df['reward'], window_size)\n",
- " df['smooth_reward'].plot(**smooth_kwargs, secondary_y=True, color=\"g\") \n",
- " ax.grid(True, which='minor', axis='x', alpha=0.3)\n",
- " \n",
- " \n",
- "def plot_2d_embeddings(top1, normalized_macs):\n",
- " plt.figure(figsize=(15,7)) \n",
- " plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
- " plt.xlabel('Normalized MACs')\n",
- " plt.ylabel('Top1 Accuracy')\n",
- "\n",
- " # Create the formatter using the function to_percent. This multiplies all the\n",
- " # default labels by 100, making them all percentages\n",
- " formatter = FuncFormatter(to_percent)\n",
- "\n",
- " # Set the formatter\n",
- " plt.gca().yaxis.set_major_formatter(formatter)\n",
- " plt.gca().xaxis.set_major_formatter(formatter)\n",
- "\n",
- " # Use color gradients to show the \"age\" of the network:\n",
- " # Lighter networks were discovered earlier than darker ones.\n",
- " color_grad = [str(1-i/len(top1)) for i in range(len(top1))]\n",
- " plt.scatter(normalized_macs, top1, color=color_grad, s=80, edgecolors='gray');\n",
- "\n",
- " \n",
- "INTERVAL = 100 # Animation speed\n",
- "WINDOW = 20\n",
- "\n",
- "font = {'family': 'serif',\n",
- " 'color': 'darkred',\n",
- " 'weight': 'normal',\n",
- " 'alpha': 0.50,\n",
- " 'size': 32,\n",
- " }\n",
- "\n",
- "# Based on these two helpful example code: \n",
- "# https://stackoverflow.com/questions/9401658/how-to-animate-a-scatter-plot\n",
- "# http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/.\n",
- "# Specifically, the use of IPython.display is missing from the first example, but most of the animation code\n",
- "# leverages code from there.\n",
- "class AnimatedScatter(object):\n",
- " \"\"\"An animated scatter plot using matplotlib.animations.FuncAnimation.\"\"\"\n",
- " def __init__(self, xdata, ydata):\n",
- " assert len(xdata) == len(ydata)\n",
- " self.numpoints = len(xdata)\n",
- " self.xdata = xdata\n",
- " self.ydata = ydata\n",
- " self.stream = self.data_stream()\n",
- "\n",
- " # Setup the figure and axes...\n",
- " self.fig, self.ax = plt.subplots(figsize=(15,7))\n",
- " # Then setup FuncAnimation.\n",
- " self.ani = animation.FuncAnimation(self.fig, self.update, interval=INTERVAL,\n",
- " frames=self.numpoints-2, \n",
- " init_func=self.setup_plot, blit=True)\n",
- "\n",
- " def setup_plot(self):\n",
- " \"\"\"Initialize drawing of the scatter plot.\"\"\"\n",
- " x, y, s, c = next(self.stream)\n",
- " #self.annot = self.ax.annotate(\"txt\", (10, 10))\n",
- " self.scat = self.ax.scatter(x, y, c=c, s=s, animated=False)\n",
- " self.scat.set_edgecolors('gray')\n",
- " self.scat.set_cmap('gray')\n",
- " self.width = max(self.xdata) - min(self.xdata) + 4\n",
- " self.height = max(self.ydata) - min(self.ydata) + 4\n",
- " self.ax.axis([min(self.xdata)-2, max(self.xdata)+2, \n",
- " min(self.ydata)-2, max(self.ydata)+2])\n",
- " \n",
- " self.annot = self.ax.text(min(self.xdata) + self.width/2, \n",
- " min(self.xdata) + self.height/2, \n",
- " \"\", fontdict=font)\n",
- " # For FuncAnimation's sake, we need to return the artist we'll be using\n",
- " # Note that it expects a sequence of artists, thus the trailing comma.\n",
- " return self.scat, \n",
- "\n",
- " def data_stream(self):\n",
- " numpoints = 0#len(self.xdata)\n",
- " colors = []\n",
- " xxx = 0\n",
- " while True:\n",
- " numpoints += 1\n",
- " win_len = min(WINDOW, numpoints)\n",
- " data = np.ndarray((4, win_len))\n",
- " start = max(0,numpoints-WINDOW-1)\n",
- " data[0, :] = self.xdata[start:start+win_len]\n",
- " data[1, :] = self.ydata[start:start+win_len]\n",
- " data[2, :] = [70] * win_len # point size\n",
- " #data[3, :] = [np.random.random() for p in range(numpoints)] # color\n",
- " # The color of the points is a gradient with larger values for \"younger\" points.\n",
- " # At each new frame we show one more point, and \"age\" each existing point by incrementaly \n",
- " # reducing its color gradient.\n",
- " data[3, :] = [(1-i/(win_len+1)) for i in range(win_len)] \n",
- " yield data\n",
- "\n",
- " def update(self, i): \n",
- " \"\"\"Update the scatter plot.\"\"\"\n",
- " data = next(self.stream)\n",
- " self.annot.set_text(i)\n",
- " i = i % len(data)\n",
- " \n",
- " # Set x and y data\n",
- " xy = [(data[0,i], data[1,i]) for i in range(len(data[0,:]))]\n",
- " self.scat.set_offsets(xy)\n",
- " \n",
- " # Set colors\n",
- " self.scat.set_array(data[3])\n",
- " \n",
- " # We need to return the updated artist for FuncAnimation to draw..\n",
- " # Note that it expects a sequence of artists, thus the trailing comma.\n",
- " return self.scat, self.annot\n",
- "\n",
- " def show(self):\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Results\n",
- "\n",
- "Below I present the results of a single execution. There is a substantial variance between the experiment executions, but most conclude similarly to this experiment."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Read the results log files\n",
- "\n",
- "The code below reads the log file of your selected experiment. To change the path to the file you will need to open the code cell and change its content."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "pd.set_option('display.max_colwidth', 150)\n",
- "\n",
- "fname = \"sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv\"\n",
- "#fname = \"sample_logs/coach_rl/AMC-Plain20___2018.12.29-113105/amc.csv\"\n",
- "\n",
- "df = pd.read_csv(fname)\n",
- "#print(df.loc[df['reward'].idxmax()])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Plot experiment performance"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Figure size 1080x504 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "50d9908acbe8456bb31c65411b0821c2",
- "version_major": 2,
- "version_minor": 0
- },
- "text/html": [
- "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
- "<p>\n",
- " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
- " Widgets Documentation</a> for setup instructions.\n",
- "</p>\n",
- "<p>\n",
- " If you're reading this message in another frontend (for example, a static\n",
- " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "</p>\n"
- ],
- "text/plain": [
- "interactive(children=(IntSlider(value=10, description='window_size', max=50, step=5), Checkbox(value=True, description='top1'), Checkbox(value=True, description='macs'), Checkbox(value=False, description='params'), Checkbox(value=True, description='reward'), Output()), _dom_classes=('widget-interact',))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.figure(figsize=(15,7))\n",
- "#print(plt.style.available)\n",
- "#plt.style.use('bmh')\n",
- "\n",
- "@interact(window_size=(0,50,5), top1=True, macs=True, params=False, reward=True)\n",
- "def plot_performance_proxy(window_size=10, top1=True, macs=True, params=False, reward=True):\n",
- " plot_performance(0.15, window_size, top1, macs, params, reward)\n",
- " \n",
- "#plot_performance(0.15, window_size=10, top1=True, macs=True, params=False, reward=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "What do we see?\n",
- " - The networks stay within the alloted 50% MACs compute budget.\n",
- " - During the exploration phase (heatup) there's quite a lot of noise and we see some promising Top1 accuracy results, and as the noise is reduced and the network begins learning we see more signs of hope, but alas, the Reward and Top1 drop and never recover.\n",
- " - What happened? The agent learns very quickly to maximize the compute budget (50% MACs) and stops producing smaller networks. But why can't it reach the high Reward values it encountered during the exploration?? There are some clues below."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Sample some networks\n",
- "\n",
- "Let's look at the networks with the best top1 accuracy, and see if they share geometrical attributes.\n",
- "\n",
- "We sort the discovered networks by their Top1 accuracy and display the density of each layer in the networks. We can see that the layer densities are distributed differently in each of the networks. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x504 with 8 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "top1_sorted_df = df.sort_values(by=['top1'], ascending=False)\n",
- "nrows = 2; ncols = 4\n",
- "f, axarr = plt.subplots(nrows, ncols, figsize=(15,7))\n",
- "for i in range(0, nrows * ncols):\n",
- " plot_layer_densities(top1_sorted_df, i, ax=axarr[i//ncols, i%ncols], color='g')\n",
- " # Fine-tune figure; make subplots farther from each other.\n",
- " f.subplots_adjust(hspace=0.6, wspace=0.4)\n",
- "\n",
- "#pd.set_option('display.max_colwidth', -1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's sample networks from episodes 251 through 274 - and notice what happens from about episode 126: the first layers are very dense (~0%-10% sparse) and the following layers have higher sparsities.\n",
- "\n",
- "What's going on? Ironically, the agent learns to minimize pruning actions (minimize sparsity) so it generates dense/wide layers until the environment has to step-in and increase the actions so that the final network's compute complexity remains within the 50% MACs budget. Clearly, these networks (dense/wide early layers followed by sparse/shallow layers) have low Top1 accuracies, but it's too late: the Agent has learned one action - very low sparsity. And so this goes on forever.\n",
- "\n",
- "Why did the Agent learn this? It's as if there is only one state to which the Agent learned one action.\n",
- "Either the agent is confused by the observations and all states look the same, or all actions are generated by the same process."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x1080 with 24 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Display networks in chronological order (of time of discovery)\n",
- "nrows = 6; ncols = 4\n",
- "first_network = 185 # 115\n",
- "f, axarr = plt.subplots(nrows, ncols, figsize=(15,15))\n",
- "for i in range(0, nrows * ncols):\n",
- " plot_layer_densities(df, first_network + i, ax=axarr[i//ncols, i%ncols])\n",
- " # Fine-tune figure; make subplots farther from each other.\n",
- "f.subplots_adjust(hspace=0.6, wspace=0.4)\n",
- "f.tight_layout()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And here's the **smoking gun**: these are the actions that the DDPG agent generated, before they were constrained by the AMC algorithm (i.e. function ```DistillerWrapperEnvironment.get_action```). Here we can clearly see that the Agent learned to emit low-sparsity actions.\n",
- "\n",
- "Using the interactive GUI you can play back the action trajectories that the Actor emitted."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "29e0b8b8a8b74f82abe960f6b59b63e4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/html": [
- "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
- "<p>\n",
- " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
- " that the widgets JavaScript is still loading. If this message persists, it\n",
- " likely means that the widgets JavaScript library is either not installed or\n",
- " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
- " Widgets Documentation</a> for setup instructions.\n",
- "</p>\n",
- "<p>\n",
- " If you're reading this message in another frontend (for example, a static\n",
- " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
- " it may mean that your frontend doesn't currently support widgets.\n",
- "</p>\n"
- ],
- "text/plain": [
- "interactive(children=(IntSlider(value=0, description='episode', max=459), Output()), _dom_classes=('widget-interact',))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "@interact(episode=(0,len(df.index)-1,1))\n",
- "def display_ddpg_actions(episode=0):\n",
- " f = plt.figure(figsize=(15,7))\n",
- " ax1 = f.add_subplot(111)\n",
- " ax2 = ax1.twiny()\n",
- " ax2.set_xlim((1,len(df.index)))\n",
- " record = df.iloc[episode]\n",
- " layer_sparsities = record[\"agent_action_history\"]\n",
- " layer_sparsities = layer_sparsities[1:-1].split(\",\")\n",
- " layer_densities = [1.- float(sparsity) for sparsity in layer_sparsities]\n",
- " ax1.bar(range(len(layer_densities)), layer_densities, color=\"gray\")\n",
- " ax2.plot(df['top1']/100)\n",
- " plt.title(\"DDPG Pruning Action History\\nEpisode:{} - Top1:{:.1f}% - MACs:{:.1f}%\\n\\n\\n\".format(\n",
- " record['episode'], record['top1'], record['normalized_macs']))\n",
- " ax1.set_xlabel(\"Layer\")\n",
- " ax2.set_xlabel(\"Episode\")\n",
- " plt.ylim([0,1])\n",
- " plt.ylabel(\"Depth/Density (%)\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Network 2D embeddings\n",
- "\n",
- "Let's create an embedding of the networks AMC discovers over the course of each experiment session. Each network is projected onto a 2D plane mapping the Top1 accuracy versus the compute budget, and is represented by a small circle. I used gradient-color-coding to show the relative phase where each network is discovered. Lighter circles are networks discovered early in the search, darker networks are discovered later."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x504 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "top1 = df['top1']\n",
- "normalized_macs = df['normalized_macs']\n",
- "plot_2d_embeddings(top1, normalized_macs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Accuracy contour"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "BIN_SIZE = 2# 0.5\n",
- "NUM_BINS = int(100 / BIN_SIZE)\n",
- "compute_bins = [None] * NUM_BINS\n",
- "color_grad_ = [\"1\" for _ in top1]\n",
- "\n",
- "idx_bins = [-1] * NUM_BINS\n",
- "\n",
- "draw_what = \"accuracy contour\"\n",
- "draw_what = \"mac contour\"\n",
- "if draw_what == \"accuracy contour\":\n",
- " for i,accuracy in enumerate(top1):\n",
- " bin_id = int(accuracy // BIN_SIZE)\n",
- " try:\n",
- " if compute_bins[bin_id] is None or compute_bins[bin_id] > normalized_macs[i]:\n",
- " compute_bins[bin_id] = normalized_macs[i]\n",
- " idx_bins[bin_id] = i\n",
- " except TypeError:\n",
- " pass\n",
- "else:\n",
- " for i,compute in enumerate(normalized_macs):\n",
- " bin_id = int(compute // BIN_SIZE)\n",
- " try:\n",
- " #print(bin_id)\n",
- " if compute_bins[bin_id] is None or compute_bins[bin_id] < top1[i]:\n",
- " compute_bins[bin_id] = top1[i]\n",
- " idx_bins[bin_id] = i\n",
- " except TypeError:\n",
- " pass\n",
- " \n",
- "for i in idx_bins:\n",
- " if i != -1:\n",
- " color_grad_[i] = \"red\"\n",
- "plt.scatter(normalized_macs, top1, color=color_grad_, s=80, edgecolors='gray');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Video animation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "a = AnimatedScatter(normalized_macs, top1)\n",
- "plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
- "plt.xlabel('Normalized MACs')\n",
- "plt.ylabel('Top1 Accuracy')\n",
- "#a.ani.save('amc_vgg16.mp4', fps=10, dpi=80) #Frame per second controls speed, dpi controls the quality \n",
- "rc('animation', html='html5')\n",
- "a.ani"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Performance Evaluation\n",
- "OK, one thing stands-out: the best Top1 we see during our traversal of the compressed network sub-space is below 80%, while the AMC paper reports a Test accuracy of 86%. The appendix to this notebook explains why this is so, but for now let's see if this is a problem: after all, what we really care about is reproducing the 90.2% Top1 score AMC reports on the fine-tuned network.\n",
- "\n",
- "So let's get to it: Here are the results of fine-tuning for 30, 50, and 60 epochs. At 60 epochs of fine-tuning, the Top1 is 88.42-90.18 = -1.76% off of the original Top1. Here too, I am not able to get the paper's results (too many epochs; Top1 is not up to par).\n",
- "\n",
- "```\n",
- "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar10 --resume=logs/AMC-Plain20-algo1_more_training___2018.12.29-033839/BEST_adc_episode_194_checkpoint.pth.tar --lr=0.005 --epochs=30\n",
- "```\n",
- "==> Top1: 87.790 Top5: 99.370 Loss: 0.400\n",
- "```\n",
- "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar10 --resume=logs/AMC-Plain20-algo1_more_training___2018.12.29-033839/BEST_adc_episode_194_checkpoint.pth.tar --lr=0.005 --epochs=50\n",
- "```\n",
- "==> Top1: 88.310 Top5: 99.440 Loss: 0.402\n",
- "```\n",
- "time python3 compress_classifier.py --arch=plain20_cifar ../../../data.cifar10 --resume=logs/AMC-Plain20-algo1_more_training___2018.12.29-033839/BEST_adc_episode_194_checkpoint.pth.tar --lr=0.005 --epochs=60\n",
- "```\n",
- "==> **Top1: 88.420** Top5: 99.390 Loss: 0.416\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Appendix: on the relative pruning robustness of Plain20 and VGG16 (CIFAR)\n",
- "\n",
- "The diagrams below show the relative pruning sensitivity of Plain20 and VGG16 at 20% filter sparsity.\n",
- "\n",
- "It is evident that VGG16 is much more robust compared to Plain20."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1440x720 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1440x720 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "df_plain20_sensitivity = pd.read_csv('../sensitivity-analysis/plain20-cifar/plain20_cifar_sensitivity_filters.csv')\n",
- "df_vgg16_sensitivity = pd.read_csv('../sensitivity-analysis/vgg16-cifar/vgg16_cifar_sensitivity_filters.csv')\n",
- "df_plain20_sensitivity['sparsity'] = round(df_plain20_sensitivity['sparsity'], 2)\n",
- "df_vgg16_sensitivity['sparsity'] = round(df_vgg16_sensitivity['sparsity'], 2)\n",
- "\n",
- "def plot_sensitivity(df, level, acc, name):\n",
- " filtered = df[df.sparsity == level]\n",
- " #s = filtered.style.apply(highlight_min_max)\n",
- " \n",
- " param_names = filtered['parameter']\n",
- " \n",
- " # Plot the sensitivities\n",
- " x = range(filtered[acc].shape[0])\n",
- " y = filtered[acc].values.tolist()\n",
- " fig = plt.figure(figsize=(20,10))\n",
- " plt.plot(x, y, label=param_names, marker=\"o\", markersize=10, markerfacecolor=\"C1\")\n",
- " plt.ylabel(str(acc))\n",
- " plt.xlabel('parameter')\n",
- " plt.xticks(rotation='vertical')\n",
- " plt.xticks(x, param_names)\n",
- " plt.title('{} Pruning Sensitivity Per Layer (at {:.0f}% sparsity)'.format(name, level*100))\n",
- "\n",
- "plot_sensitivity(df_plain20_sensitivity, 0.2, 'top1', name=\"Plain20 (CIFAR)\") \n",
- "plot_sensitivity(df_vgg16_sensitivity, 0.2, 'top1', name=\"VGG16 (CIFAR)\") "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/automated_deep_compression/fine_tune.yaml b/examples/automated_deep_compression/fine_tune.yaml
deleted file mode 100755
index a7f252d..0000000
--- a/examples/automated_deep_compression/fine_tune.yaml
+++ /dev/null
@@ -1,15 +0,0 @@
-lr_schedulers:
- training_lr:
- class: StepLR # ReduceLROnPlateau
- step_size: 10
- gamma: 0.2
- #mode: max
- #patience: 5
- #factor: 0.1
-
-policies:
- - lr_scheduler:
- instance_name: training_lr
- starting_epoch: 0
- ending_epoch: 200000
- frequency: 1
diff --git a/examples/automated_deep_compression/ppo-amc-results.ipynb b/examples/automated_deep_compression/ppo-amc-results.ipynb
deleted file mode 100644
index e1e3455..0000000
--- a/examples/automated_deep_compression/ppo-amc-results.ipynb
+++ /dev/null
@@ -1,577 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "# Using Clipped PPO for Automated Model Compression\n",
- "\n",
- "\n",
- "Continuing on the work of [AMC](https://arxiv.org/abs/1802.03494) we replace DDPG with Clipped PPO.\n",
- "\n",
- "Results are interesting and encouraging as there is learning. However, this is less sample-efficient compared to DDPG, and therefore takes longer.\n",
- "\n",
- "We search for a 50%-MACs-constrained (FLOPs-constrained) Plain20. From Greedy Search algorithm we know that there exists a 50%-MACs-constrained Plain20 that can provide Top1=90%. The current fine-tuned Plain20 model from our PPO experiments has a Top1=89%."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Experiment setup\n",
- "\n",
- "\n",
- "### Clipped PPO configuration\n",
- "\n",
- "### Distiller Clipped PPO AMC experiments\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Notebook code\n",
- "\n",
- "Skip this part - it is necessary only for creating the diagrams. You may also toggle the code-view button."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from IPython.display import HTML\n",
- "HTML('''<script>\n",
- "code_show=true; \n",
- "function code_toggle() {\n",
- " if (code_show){\n",
- " $('div.input').hide();\n",
- " } else {\n",
- " $('div.input').show();\n",
- " }\n",
- " code_show = !code_show\n",
- "} \n",
- "$( document ).ready(code_toggle);\n",
- "</script>\n",
- "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off code view\"></form>''')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "\n",
- "import numpy as np\n",
- "import pandas as pd \n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib \n",
- "import csv\n",
- "from matplotlib.ticker import FuncFormatter\n",
- "import ipywidgets as widgets\n",
- "from ipywidgets import interactive, interact, Layout\n",
- "import matplotlib.pylab as pylab\n",
- "import matplotlib.animation as animation\n",
- "from matplotlib import animation, rc\n",
- "\n",
- "\n",
- "\n",
- "#plt.style.use('seaborn') # pretty matplotlib plots\n",
- "\n",
- "params = {'legend.fontsize': 'x-large',\n",
- " 'figure.figsize': (15, 7),\n",
- " 'axes.labelsize': 'x-large',\n",
- " 'axes.titlesize':'xx-large',\n",
- " 'xtick.labelsize':'x-large',\n",
- " 'ytick.labelsize':'x-large'}\n",
- "pylab.rcParams.update(params)\n",
- "\n",
- "\n",
- "def to_percent(y, position):\n",
- " # Ignore the passed in position. This has the effect of scaling the default\n",
- " # tick locations.\n",
- " if y < 1:\n",
- " y = 100 * y\n",
- " s = \"{:.1f}\".format(y)\n",
- "\n",
- " # The percent symbol needs escaping in latex\n",
- " if matplotlib.rcParams['text.usetex'] is True:\n",
- " return s + r'$\\%$'\n",
- " else:\n",
- " return s + '%'\n",
- " \n",
- "# Widen the cells to get entire rows in the screen.\n",
- "from IPython.core.display import display, HTML\n",
- "#display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
- "\n",
- "\n",
- "def plot_layer_densities(df, idx, action_type='action_history', ax=None, color=None):\n",
- " if ax is None:\n",
- " plt.figure()\n",
- " ax = plt\n",
- " \n",
- " record = df.iloc[idx]\n",
- " layer_sparsities = record[action_type]\n",
- " layer_sparsities = layer_sparsities[1:-1].split(\",\")\n",
- " layer_densities = [1.- float(sparsity) for sparsity in layer_sparsities]\n",
- " ax.bar(range(len(layer_densities)), layer_densities, color=color)\n",
- " ax.set_title(\"Ep:{} - Top1:{:.1f}%\\nMACs:{:.1f}%\".format(record['episode'], \n",
- " record['top1'], \n",
- " record['normalized_macs']))\n",
- " \n",
- " \n",
- "def smooth(data, win_size):\n",
- " win_size = max(1, win_size)\n",
- " return [np.mean(data[max(0, i-win_size):i]) for i in range(len(data))]\n",
- "\n",
- "\n",
- "def plot_performance(alpha, window_size, top1, macs, params, reward, start=0, end=-1):\n",
- " plot_kwargs = {\"figsize\":(15,7), \"lw\": 1, \"alpha\": alpha, \"title\": \"Performance Data\"}\n",
- " smooth_kwargs = {\"lw\": 2 if window_size > 0 else 1, \"legend\": True}\n",
- " if macs:\n",
- " ax = df['normalized_macs'][start:end].plot(**plot_kwargs, color=\"r\")\n",
- " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
- " #ax.set_ylim([0,100])\n",
- " df['smooth_normalized_macs'] = smooth(df['normalized_macs'], window_size)\n",
- " df['smooth_normalized_macs'][start:end].plot(**smooth_kwargs, color=\"r\")\n",
- " if top1:\n",
- " ax = df['top1'][start:end].plot(**plot_kwargs, color=\"b\", grid=True)\n",
- " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
- " df['smooth_top1'] = smooth(df['top1'], window_size)\n",
- " df['smooth_top1'][start:end].plot(**smooth_kwargs, color=\"b\")\n",
- " if params:\n",
- " ax = df['normalized_nnz'][start:end].plot(**plot_kwargs, color=\"black\")\n",
- " ax.set(xlabel=\"Episode\", ylabel=\"(%)\")\n",
- " df['smooth_normalized_nnz'] = smooth(df['normalized_nnz'], window_size)\n",
- " df['smooth_normalized_nnz'][start:end].plot(**smooth_kwargs, color=\"black\") \n",
- " if reward:\n",
- " ax = df['reward'][start:end].plot(**plot_kwargs, secondary_y=True, color=\"g\")\n",
- " ax.set(xlabel=\"Episode\", ylabel=\"reward\")\n",
- " df['smooth_reward'] = smooth(df['reward'], window_size)\n",
- " df['smooth_reward'][start:end].plot(**smooth_kwargs, secondary_y=True, color=\"g\") \n",
- " #ax.set_ylim([0,100])\n",
- " ax.grid(True, which='minor', axis='x', alpha=0.3)\n",
- " \n",
- " \n",
- "def plot_2d_embeddings(top1, normalized_macs):\n",
- " plt.figure(figsize=(15,7)) \n",
- " plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
- " plt.xlabel('Normalized MACs')\n",
- " plt.ylabel('Top1 Accuracy')\n",
- "\n",
- " # Create the formatter using the function to_percent. This multiplies all the\n",
- " # default labels by 100, making them all percentages\n",
- " formatter = FuncFormatter(to_percent)\n",
- "\n",
- " # Set the formatter\n",
- " plt.gca().yaxis.set_major_formatter(formatter)\n",
- " plt.gca().xaxis.set_major_formatter(formatter)\n",
- "\n",
- " # Use color gradients to show the \"age\" of the network:\n",
- " # Lighter networks were discovered earlier than darker ones.\n",
- " color_grad = [str(1-i/len(top1)) for i in range(len(top1))]\n",
- " plt.scatter(normalized_macs, top1, color=color_grad, s=80, edgecolors='gray');\n",
- "\n",
- " \n",
- "INTERVAL = 30 # Animation speed\n",
- "WINDOW = 20\n",
- "\n",
- "font = {'family': 'serif',\n",
- " 'color': 'darkred',\n",
- " 'weight': 'normal',\n",
- " 'alpha': 0.50,\n",
- " 'size': 32,\n",
- " }\n",
- "\n",
- "# Based on these two helpful example code: \n",
- "# https://stackoverflow.com/questions/9401658/how-to-animate-a-scatter-plot\n",
- "# http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/.\n",
- "# Specifically, the use of IPython.display is missing from the first example, but most of the animation code\n",
- "# leverages code from there.\n",
- "class AnimatedScatter(object):\n",
- " \"\"\"An animated scatter plot using matplotlib.animations.FuncAnimation.\"\"\"\n",
- " def __init__(self, xdata, ydata):\n",
- " assert len(xdata) == len(ydata)\n",
- " self.numpoints = len(xdata)\n",
- " self.xdata = xdata\n",
- " self.ydata = ydata\n",
- " self.stream = self.data_stream()\n",
- "\n",
- " # Setup the figure and axes...\n",
- " self.fig, self.ax = plt.subplots(figsize=(15,7))\n",
- " # Then setup FuncAnimation.\n",
- " self.ani = animation.FuncAnimation(self.fig, self.update, interval=INTERVAL,\n",
- " frames=self.numpoints-2, \n",
- " init_func=self.setup_plot, blit=True)\n",
- "\n",
- " def setup_plot(self):\n",
- " \"\"\"Initialize drawing of the scatter plot.\"\"\"\n",
- " x, y, s, c = next(self.stream)\n",
- " #self.annot = self.ax.annotate(\"txt\", (10, 10))\n",
- " self.scat = self.ax.scatter(x, y, c=c, s=s, animated=False)\n",
- " self.scat.set_edgecolors('gray')\n",
- " self.scat.set_cmap('gray')\n",
- " self.width = max(self.xdata) - min(self.xdata) + 4\n",
- " self.height = max(self.ydata) - min(self.ydata) + 4\n",
- " self.ax.axis([min(self.xdata)-2, max(self.xdata)+2, \n",
- " min(self.ydata)-2, max(self.ydata)+2])\n",
- " \n",
- " self.annot = self.ax.text(min(self.xdata) + self.width/2, \n",
- " min(self.xdata) + self.height/2, \n",
- " \"\", fontdict=font)\n",
- " # For FuncAnimation's sake, we need to return the artist we'll be using\n",
- " # Note that it expects a sequence of artists, thus the trailing comma.\n",
- " return self.scat, \n",
- "\n",
- " def data_stream(self):\n",
- " numpoints = 0#len(self.xdata)\n",
- " colors = []\n",
- " xxx = 0\n",
- " while True:\n",
- " numpoints += 1\n",
- " win_len = min(WINDOW, numpoints)\n",
- " data = np.ndarray((4, win_len))\n",
- " start = max(0,numpoints-WINDOW-1)\n",
- " data[0, :] = self.xdata[start:start+win_len]\n",
- " data[1, :] = self.ydata[start:start+win_len]\n",
- " data[2, :] = [70] * win_len # point size\n",
- " #data[3, :] = [np.random.random() for p in range(numpoints)] # color\n",
- " # The color of the points is a gradient with larger values for \"younger\" points.\n",
- " # At each new frame we show one more point, and \"age\" each existing point by incrementaly \n",
- " # reducing its color gradient.\n",
- " data[3, :] = [(1-i/(win_len+1)) for i in range(win_len)] \n",
- " yield data\n",
- "\n",
- " def update(self, i): \n",
- " \"\"\"Update the scatter plot.\"\"\"\n",
- " data = next(self.stream)\n",
- " self.annot.set_text(i)\n",
- " i = i % len(data)\n",
- " \n",
- " # Set x and y data\n",
- " xy = [(data[0,i], data[1,i]) for i in range(len(data[0,:]))]\n",
- " self.scat.set_offsets(xy)\n",
- " \n",
- " # Set colors\n",
- " self.scat.set_array(data[3])\n",
- " \n",
- " # We need to return the updated artist for FuncAnimation to draw..\n",
- " # Note that it expects a sequence of artists, thus the trailing comma.\n",
- " return self.scat, self.annot\n",
- "\n",
- " def show(self):\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Results\n",
- "\n",
- "Below I present the results of a single execution. There is a substantial variance between the experiment executions, but most conclude similarly to this experiment."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Read the results log files\n",
- "\n",
- "The code below reads the log file of your selected experiment. To change the path to the file you will need to open the code cell and change its content."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "pd.set_option('display.max_colwidth', 150)\n",
- "\n",
- "fname = \"sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv\"\n",
- "fname = \"../classifier_compression/logs_stash/resnet56-amc___2019.02.01-053412/amc.csv\"\n",
- "#fname = \"../classifier_compression/logs/resnet20___2019.01.29-102912/amc.csv\"\n",
- "#fname = \"../classifier_compression/logs/resnet56-amc-0.1_epoch___2019.02.02-212945/amc.csv\"\n",
- "#fname = \"../classifier_compression/logs/resnet20___2019.02.03-210001/amc.csv\"\n",
- "#fname = \"../classifier_compression/logs_stash/plain20-amc-random-2___2019.02.06-063954/amc.csv\"\n",
- "fname = \"../classifier_compression/logs/ame=plain20-amc-reconstruct___2019.02.22-180846/amc.csv\"\n",
- "#fname = \"../classifier_compression/logs/plain20-90.84_top1-amc___2019.02.23-143702/amc.csv\"\n",
- "\n",
- "df = pd.read_csv(fname)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Plot experiment performance"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false,
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "outputs": [],
- "source": [
- "plt.figure(figsize=(15,7))\n",
- "#print(plt.style.available)\n",
- "#plt.style.use('bmh')\n",
- "\n",
- "@interact(window_size=(0,50,5), top1=True, macs=True, params=False, reward=True)\n",
- "def plot_performance_proxy(window_size=10, top1=True, macs=True, params=False, reward=True):\n",
- " plot_performance(0.15, window_size, top1, macs, params, reward)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "outputs": [],
- "source": [
- "plot_performance(0.15, window_size=20, top1=True, macs=True, params=False, reward=True, start=0, end=1000)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "What do we see?\n",
- "- If we zoom in on the first 600 episodes, we see how the reward starts rising as the agent learns to retain as many compute resources (MACs) as possible. Does this occur with other models?\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "subslide"
- }
- },
- "source": [
- "### Sample some networks\n",
- "\n",
- "Let's look at the networks with the best top1 accuracy, and see if they share geometrical attributes.\n",
- "\n",
- "We sort the discovered networks by their Top1 accuracy and display the density of each layer in the networks. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false,
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "outputs": [],
- "source": [
- "top1_sorted_df = df.sort_values(by=['reward'], ascending=False)\n",
- "nrows = 2; ncols = 4\n",
- "f, axarr = plt.subplots(nrows, ncols, figsize=(15,7))\n",
- "for i in range(0, nrows * ncols):\n",
- " plot_layer_densities(top1_sorted_df, i, ax=axarr[i//ncols, i%ncols], color='g')\n",
- " # Fine-tune figure; make subplots farther from each other.\n",
- " f.subplots_adjust(hspace=0.6, wspace=0.4)\n",
- "\n",
- "#pd.set_option('display.max_colwidth', -1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Per-layer filter density distribution - top 10% networks"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "top10pct = top1_sorted_df[:int(len(df.index) * 0.1)]\n",
- "#top10pct = df[int(len(df.index) * 0.95):]\n",
- "\n",
- "layer_densities_list = []\n",
- "for index, row in top10pct.iterrows():\n",
- " layer_sparsities = row['action_history']\n",
- " layer_sparsities = layer_sparsities[1:-1].split(\",\") # convert from string to list\n",
- " layer_densities = [1. - float(sparsity) for sparsity in layer_sparsities]\n",
- " layer_densities_list.append(layer_densities)\n",
- "\n",
- "layer_densities = np.array(layer_densities_list)\n",
- "mean = layer_densities.mean(axis=0)\n",
- "std = layer_densities.std(axis=0)\n",
- "\n",
- "\n",
- "# Draw the bar diagram of the layer densities\n",
- "fig, ax = plt.subplots(figsize=(15,7.5))\n",
- "ax.set_title(\"Best sampled models (90th percentile)\\nPer-layer filter density (mean and std)\")\n",
- "xpos = [i for i in range(len(mean))]\n",
- "ax.bar(xpos, mean, yerr=std, capsize=10, alpha=0.5, ecolor='black')\n",
- "ax.set_ylabel('Filter density')\n",
- "ax.set_xticks(xpos)\n",
- "#ax.set_xticklabels(layer_names)\n",
- "ax.yaxis.grid(True)\n",
- "#plt.tight_layout()\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Network 2D embeddings\n",
- "\n",
- "Let's create an embedding of the networks AMC discovers over the course of each experiment session. Each network is projected onto a 2D plane mapping the Top1 accuracy versus the compute budget, and is represented by a small circle. I used gradient-color-coding to show the relative phase where each network is discovered. Lighter circles are networks discovered early in the search, darker networks are discovered later."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "top1 = df['top1']\n",
- "normalized_macs = df['normalized_macs']\n",
- "plot_2d_embeddings(top1, normalized_macs)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Video animation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "a = AnimatedScatter(normalized_macs, top1)\n",
- "plt.title('Projection of Discovered Networks ({})'.format(len(top1))) \n",
- "plt.xlabel('Normalized MACs')\n",
- "plt.ylabel('Top1 Accuracy')\n",
- "#a.ani.save('amc_vgg16.mp4', fps=10, dpi=80) #Frame per second controls speed, dpi controls the quality \n",
- "rc('animation', html='html5')\n",
- "a.ani"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Relative import of code from distiller, w/o installing the package\n",
- "import os\n",
- "import sys\n",
- "module_path = os.path.abspath(os.path.join('../..'))\n",
- "if module_path not in sys.path:\n",
- " sys.path.append(module_path)\n",
- "module_path = os.path.abspath(os.path.join('../../..'))\n",
- "if module_path not in sys.path:\n",
- " sys.path.append(module_path)\n",
- "import models\n",
- "import distiller.utils\n",
- "import distiller\n",
- "import apputils.checkpoint \n",
- "from models import create_model\n",
- " \n",
- "weights_model_a = create_model(False, 'cifar10', 'plain20_cifar')\n",
- "weights_model_b = create_model(False, 'cifar10', 'plain20_cifar')\n",
- "checkpoint_file = \"../examples/classifier_compression/checkpoint.plain20_cifar.pth.tar\"\n",
- "load_checkpoint(weights_model_a, checkpoint_file)\n",
- "#checkpoint_file = \"../examples/classifier_compression/logs/2019.02.23-130915/best.pth.tar\"\n",
- "checkpoint_file = \"../examples/classifier_compression/logs/2019.02.24-024349/best.pth.tar\"\n",
- "load_checkpoint(weights_model_b, checkpoint_file)\n",
- "\n",
- "params_names = conv_param_names(weights_model_a)\n",
- "\n",
- "def get_filter_l1mags(param):\n",
- " view_filters = param.view(param.size(0), -1)\n",
- " filter_size = view_filters.shape[1]\n",
- " filter_mags_l1 = to_np(view_filters.norm(p=1, dim=1).div(filter_size))\n",
- " return filter_mags_l1\n",
- "\n",
- "def view_weights(pname):\n",
- " param_a = weights_model_a.state_dict()[pname]\n",
- " filter_mags_l1_a = get_filter_l1mags(param_a)\n",
- " \n",
- " param_b = weights_model_b.state_dict()[pname]\n",
- " filter_mags_l1_b = get_filter_l1mags(param_b)\n",
- " \n",
- "# std = np.std(filter_mags_l1)\n",
- "# mean = np.mean(filter_mags_l1)\n",
- " plt.figure(figsize=[15,7.5])\n",
- " plt.hist(filter_mags_l1_a, alpha=0.5, label='a')\n",
- " plt.hist(filter_mags_l1_b, alpha=0.5, label='b')\n",
- "# plt.title(\"mean: {:.4f} std: {:.4f}\".format(mean, std))\n",
- " plt.xlabel('Normalized Fliter L1-norm')\n",
- " plt.ylabel('Frequency')\n",
- " plt.legend()\n",
- "\n",
- "params_dropdown = widgets.Dropdown(description='Weights:', options=params_names, layout=Layout(width='40%'))\n",
- "interact(view_weights, pname=params_dropdown);\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "celltoolbar": "Slideshow",
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/automated_deep_compression/sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv b/examples/automated_deep_compression/sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv
deleted file mode 100644
index 3ebdf50..0000000
--- a/examples/automated_deep_compression/sample_logs/clipped_ppo/accuracy-guaranteed_clipped-ppo.amc.csv
+++ /dev/null
@@ -1,3153 +0,0 @@
-episode,top1,reward,total_macs,normalized_macs,normalized_nnz,ckpt_name,action_history,agent_action_history
-1,55.59,-7.471664447521032,20262528,49.96875,44.97377622377623,BEST_adc_episode_001,"[0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.53125, 0.796875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.64060384, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5325509, 0.8, 0.2, 0.2, 0.2]"
-2,48.040000000000006,-8.566173991573633,14448384,35.63068181818182,44.96705217859064,adc_episode_002,"[0.5625, 0.1875, 0.75, 0.75, 0.1875, 0.75, 0.1875, 0.59375, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875]","[0.6113952, 0.2, 0.77715176, 0.8, 0.2, 0.78101426, 0.2, 0.6172896, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2]"
-3,33.17,-11.076577929272247,15780096,38.91477272727273,55.44311457772996,adc_episode_003,"[0.375, 0.3125, 0.1875, 0.75, 0.75, 0.4375, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.4096109, 0.32922018, 0.2, 0.8, 0.8, 0.4927334, 0.2, 0.2, 0.8, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-4,21.609999999999996,-12.914522498134303,14284800,35.22727272727273,44.15344271113502,adc_episode_004,"[0.1875, 0.1875, 0.75, 0.75, 0.6875, 0.6875, 0.75, 0.1875, 0.1875, 0.375, 0.1875, 0.78125, 0.1875, 0.5625, 0.1875, 0.1875, 0.546875, 0.1875, 0.1875]","[0.2, 0.2, 0.8, 0.8, 0.6931433, 0.70121616, 0.8, 0.2, 0.2, 0.37824214, 0.20681375, 0.8, 0.2, 0.57309604, 0.2, 0.2, 0.5597267, 0.2, 0.2]"
-5,49.980000000000004,-8.342554246614586,17512704,43.1875,55.0564819795589,adc_episode_005,"[0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.78125, 0.71875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375]","[0.2, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.7320402, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4382071]"
-6,29.01,-11.890704529531282,18808704,46.38352272727273,42.41863905325444,adc_episode_006,"[0.6875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.796875, 0.1875, 0.328125, 0.21875, 0.1875]","[0.7162616, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.33758944, 0.22015022, 0.2]"
-7,29.479999999999997,-11.707539967068247,16219584,39.99857954545455,40.98305540613233,adc_episode_007,"[0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.75, 0.25, 0.1875, 0.1875, 0.1875, 0.65625, 0.1875, 0.78125, 0.796875, 0.1875, 0.1875, 0.546875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.26608127, 0.2, 0.2, 0.2, 0.67828786, 0.2, 0.8, 0.8, 0.2, 0.2, 0.54987895, 0.2, 0.2]"
-8,40.5,-9.979001044738254,19219392,47.39630681818181,45.683162990855294,adc_episode_008,"[0.1875, 0.75, 0.1875, 0.3125, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.8, 0.2, 0.34389165, 0.7206159, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.2]"
-9,26.8,-11.937110398634719,12085632,29.803977272727273,31.145777299623457,adc_episode_009,"[0.1875, 0.75, 0.75, 0.75, 0.1875, 0.1875, 0.75, 0.78125, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.796875, 0.796875, 0.1875, 0.1875, 0.265625, 0.796875]","[0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.8, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2705093, 0.8]"
-10,53.36000000000001,-7.760424979658303,16835328,41.51704545454545,39.95763851533082,adc_episode_010,"[0.5, 0.1875, 0.1875, 0.75, 0.4375, 0.1875, 0.1875, 0.1875, 0.3125, 0.25, 0.1875, 0.78125, 0.1875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.796875]","[0.5087335, 0.2, 0.22310375, 0.8, 0.4402622, 0.2, 0.2, 0.2, 0.33992076, 0.26379427, 0.2, 0.8, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.8]"
-11,24.099999999999998,-12.385581016714568,12216384,30.126420454545453,22.16581495427649,adc_episode_011,"[0.75, 0.1875, 0.75, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.6875, 0.1875, 0.71875, 0.59375, 0.1875, 0.796875, 0.796875, 0.1875]","[0.8, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5640245, 0.2, 0.7181329, 0.2, 0.7211405, 0.60042065, 0.2, 0.8, 0.8, 0.2]"
-12,41.23,-9.893052831358784,20450304,50.43181818181818,52.2626412049489,adc_episode_012,"[0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.75, 0.5625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.37657455, 0.2, 0.2, 0.2, 0.8, 0.62348944, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.75581264, 0.2, 0.2, 0.2, 0.2, 0.2]"
-13,42.78999999999999,-9.525124647257512,17011584,41.95170454545455,47.88528778913395,adc_episode_013,"[0.5, 0.1875, 0.1875, 0.4375, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.78125, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.51191026, 0.2, 0.2, 0.49817657, 0.7572117, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2]"
-14,39.870000000000005,-10.089310421432874,19368576,47.76420454545455,44.213959117805274,adc_episode_014,"[0.1875, 0.1875, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.359375, 0.640625, 0.1875, 0.1875, 0.3125, 0.1875]","[0.2, 0.2, 0.2, 0.71005416, 0.2, 0.2, 0.2, 0.70436376, 0.2, 0.2, 0.2, 0.2, 0.8, 0.3624721, 0.65216035, 0.2, 0.2, 0.32427475, 0.2]"
-15,27.969999999999995,-11.856257500430672,14078592,34.71875,37.52353415814954,adc_episode_015,"[0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.625, 0.40625, 0.78125, 0.78125, 0.78125, 0.1875, 0.6875, 0.796875, 0.1875, 0.1875, 0.484375, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.67654616, 0.40654185, 0.8, 0.8, 0.8, 0.2, 0.7004791, 0.8, 0.2, 0.2, 0.4964586, 0.2, 0.2]"
-16,40.12,-10.047611770339927,19376640,47.78409090909091,60.2373587950511,adc_episode_016,"[0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.75, 0.75, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.8, 0.2, 0.24692176, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-17,45.06,-9.15861539940759,17369280,42.83380681818182,41.531065088757394,adc_episode_017,"[0.75, 0.1875, 0.1875, 0.1875, 0.25, 0.5625, 0.3125, 0.78125, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.375, 0.1875, 0.1875, 0.328125, 0.796875, 0.1875]","[0.8, 0.2, 0.2, 0.2, 0.27555227, 0.58800066, 0.31295314, 0.8, 0.2, 0.2, 0.2, 0.56689847, 0.2, 0.37541398, 0.2, 0.2, 0.33938667, 0.8, 0.2]"
-18,36.31999999999999,-10.529167021799982,15164928,37.39772727272727,40.082033351264116,adc_episode_018,"[0.75, 0.5625, 0.1875, 0.1875, 0.1875, 0.6875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.59375, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.796875]","[0.8, 0.5770091, 0.2, 0.2, 0.2, 0.7447747, 0.2, 0.2, 0.8, 0.2, 0.2, 0.5978453, 0.8, 0.2, 0.2, 0.2, 0.2, 0.7544915, 0.8]"
-19,21.730000000000004,-12.738539566422816,11700288,28.85369318181818,15.905728886498116,adc_episode_019,"[0.5, 0.1875, 0.1875, 0.1875, 0.5625, 0.5, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.1875, 0.78125, 0.796875, 0.515625, 0.796875, 0.5, 0.796875, 0.1875]","[0.55460817, 0.2, 0.2, 0.2, 0.5978127, 0.5350738, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.8, 0.8, 0.5201223, 0.8, 0.51147085, 0.8, 0.2]"
-20,33.5,-11.036527338886724,16131456,39.78125,30.301909628832707,adc_episode_020,"[0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.78125, 0.1875, 0.796875, 0.40625, 0.5625, 0.34375, 0.1875]","[0.2, 0.2, 0.7949842, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.7903293, 0.2, 0.8, 0.41266987, 0.5654255, 0.3493107, 0.2]"
-21,61.39,-6.4316935068223335,17160192,42.31818181818181,58.354626143087685,BEST_adc_episode_021,"[0.6875, 0.75, 0.75, 0.1875, 0.375, 0.1875, 0.75, 0.1875, 0.65625, 0.46875, 0.40625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.68760145, 0.8, 0.79505247, 0.2, 0.4353529, 0.2, 0.8, 0.2, 0.6603586, 0.48413965, 0.42990598, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-22,21.96,-12.964452228547572,16396416,40.43465909090909,43.528106508875744,adc_episode_022,"[0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.75, 0.78125, 0.78125, 0.1875, 0.1875, 0.5625, 0.1875, 0.796875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.58731437, 0.2, 0.8, 0.2, 0.38924944, 0.2, 0.2, 0.2]"
-23,43.489999999999995,-9.464116403594643,18768384,46.28409090909091,39.349112426035504,adc_episode_023,"[0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.796875, 0.359375, 0.328125, 0.1875, 0.1875]","[0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.34541565, 0.2, 0.2, 0.21762732, 0.2, 0.8, 0.8, 0.2, 0.8, 0.3712739, 0.32934186, 0.2, 0.2]"
-24,29.269999999999996,-11.643951753277868,14112000,34.80113636363637,29.437869822485208,adc_episode_024,"[0.1875, 0.6875, 0.75, 0.1875, 0.1875, 0.5, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.796875, 0.1875, 0.796875, 0.1875, 0.203125]","[0.2, 0.70746064, 0.8, 0.2, 0.2, 0.53573066, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.8, 0.2, 0.20976378]"
-25,63.31,-6.158266063262887,19473984,48.02414772727273,43.63905325443787,BEST_adc_episode_025,"[0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375, 0.71875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.609375, 0.609375, 0.1875]","[0.77120274, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.44403622, 0.72465396, 0.2, 0.40372658, 0.2, 0.2, 0.2, 0.6123554, 0.6201672, 0.2]"
-26,62.980000000000004,-6.2270426381856,20191104,49.79261363636363,55.60112963959117,adc_episode_026,"[0.75, 0.75, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.28125, 0.1875, 0.1875, 0.671875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.8, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.29928565, 0.2, 0.2, 0.68256384, 0.2, 0.2, 0.2, 0.2, 0.2]"
-27,38.660000000000004,-10.145443419995624,15243840,37.59232954545455,28.06616460462614,adc_episode_027,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.25, 0.28125, 0.5625, 0.78125, 0.375, 0.1875, 0.4375, 0.1875, 0.796875, 0.1875, 0.703125, 0.640625]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2642736, 0.2840583, 0.5719728, 0.8, 0.3845428, 0.2, 0.44375256, 0.2, 0.8, 0.2, 0.7115403, 0.64615595]"
-28,62.09,-6.36943131738809,19805184,48.84090909090909,52.98547606239914,adc_episode_028,"[0.375, 0.1875, 0.375, 0.75, 0.4375, 0.625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.5, 0.1875, 0.375, 0.1875, 0.1875]","[0.43165204, 0.2, 0.40650767, 0.8, 0.48558044, 0.6425399, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.50699455, 0.2, 0.3882888, 0.2, 0.2]"
-29,25.180000000000003,-12.40036954825898,15769728,38.88920454545455,27.403846153846157,adc_episode_029,"[0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375, 0.1875, 0.1875, 0.78125, 0.1875, 0.21875, 0.1875, 0.796875, 0.1875, 0.1875, 0.796875, 0.75, 0.796875]","[0.2, 0.8, 0.2, 0.24170041, 0.2, 0.2, 0.45257598, 0.2, 0.2, 0.8, 0.2, 0.2428042, 0.2, 0.8, 0.2, 0.2, 0.8, 0.7545024, 0.8]"
-30,60.31999999999999,-6.643038077614515,18653184,46.0,49.388111888111894,adc_episode_030,"[0.3125, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.65625, 0.1875, 0.21875, 0.375, 0.78125, 0.3125, 0.1875, 0.21875, 0.1875, 0.265625, 0.1875, 0.765625]","[0.33830816, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.66983837, 0.2, 0.22613294, 0.38245153, 0.8, 0.34112826, 0.2, 0.22263649, 0.2, 0.28003466, 0.2, 0.76778597]"
-31,16.959999999999997,-13.522229531086134,11804544,29.110795454545457,34.66917697686929,adc_episode_031,"[0.4375, 0.75, 0.6875, 0.5625, 0.75, 0.75, 0.1875, 0.1875, 0.21875, 0.5, 0.1875, 0.6875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875]","[0.49089804, 0.8, 0.71200407, 0.5907516, 0.8, 0.8, 0.2, 0.2, 0.23459496, 0.5214883, 0.2, 0.6973481, 0.2, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2]"
-32,17.03,-13.54614075972182,12317760,30.376420454545457,27.023937600860677,adc_episode_032,"[0.1875, 0.75, 0.75, 0.1875, 0.5, 0.1875, 0.75, 0.78125, 0.1875, 0.1875, 0.1875, 0.25, 0.78125, 0.1875, 0.1875, 0.796875, 0.796875, 0.1875, 0.796875]","[0.21562923, 0.7814509, 0.8, 0.2, 0.5263496, 0.2, 0.7523382, 0.8, 0.2, 0.2, 0.2, 0.27498287, 0.8, 0.2, 0.2, 0.8, 0.8, 0.2, 0.8]"
-33,41.83,-9.695576046178939,17324928,42.72443181818182,52.45763851533082,adc_episode_033,"[0.75, 0.625, 0.75, 0.625, 0.625, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.8, 0.65404934, 0.8, 0.658829, 0.6818789, 0.23975264, 0.704056, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2]"
-34,43.769999999999996,-9.385780676412088,17747712,43.76704545454545,38.888515330823026,adc_episode_034,"[0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.375, 0.78125, 0.5625, 0.78125, 0.1875, 0.1875, 0.1875, 0.4375, 0.796875, 0.1875]","[0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3880465, 0.8, 0.56943935, 0.8, 0.2, 0.2, 0.2, 0.4421505, 0.8, 0.2]"
-35,55.35,-7.521983770132654,20718720,51.09375000000001,47.55580957504034,adc_episode_035,"[0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.421875, 0.1875, 0.421875, 0.40625, 0.1875]","[0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.43552947, 0.2, 0.43415412, 0.40981892, 0.2]"
-36,16.379999999999995,-13.862321233694242,15835392,39.05113636363636,33.324367939752555,adc_episode_036,"[0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.6875, 0.78125, 0.1875, 0.1875, 0.75, 0.796875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875]","[0.47178036, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.71638435, 0.8, 0.2, 0.2, 0.7531124, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2]"
-37,36.42999999999999,-10.54583741278124,16019712,39.50568181818182,47.502017213555675,adc_episode_037,"[0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.5625, 0.1875, 0.78125, 0.375, 0.78125, 0.34375, 0.1875, 0.78125, 0.1875, 0.1875, 0.4375, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.8, 0.2, 0.2, 0.58409625, 0.2, 0.8, 0.4028571, 0.8, 0.36899218, 0.2, 0.8, 0.2, 0.2, 0.44527477, 0.2, 0.2, 0.2]"
-38,43.669999999999995,-9.352913539796756,16252992,40.08096590909091,39.443249058633675,adc_episode_038,"[0.1875, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.1875, 0.1875, 0.78125, 0.78125, 0.71875, 0.4375, 0.625, 0.296875, 0.484375, 0.1875, 0.1875, 0.1875, 0.796875]","[0.2, 0.2, 0.2, 0.2, 0.6070735, 0.2, 0.2, 0.2, 0.8, 0.8, 0.74653286, 0.45894557, 0.65343326, 0.30491456, 0.49074107, 0.2, 0.2, 0.2, 0.8]"
-39,28.859999999999996,-11.791395791236006,15790464,38.94034090909091,36.36027434104357,adc_episode_039,"[0.75, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.53125, 0.1875, 0.796875, 0.1875, 0.21875, 0.1875, 0.734375, 0.1875]","[0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.5323556, 0.2, 0.8, 0.2, 0.2206606, 0.2, 0.7464783, 0.2]"
-40,70.05,-5.011598577756139,18498816,45.61931818181818,49.66715976331361,BEST_adc_episode_040,"[0.5625, 0.1875, 0.1875, 0.5, 0.75, 0.1875, 0.5, 0.375, 0.1875, 0.46875, 0.1875, 0.1875, 0.1875, 0.1875, 0.484375, 0.1875, 0.1875, 0.1875, 0.671875]","[0.58380896, 0.2, 0.2, 0.5104944, 0.79755783, 0.2, 0.5314568, 0.37845096, 0.2, 0.48620996, 0.2, 0.2, 0.2, 0.2, 0.49635315, 0.2, 0.2, 0.2, 0.6753568]"
-41,46.78,-8.919126808632523,18981504,46.809659090909086,41.22848305540614,adc_episode_041,"[0.75, 0.3125, 0.1875, 0.1875, 0.5625, 0.1875, 0.1875, 0.1875, 0.1875, 0.34375, 0.1875, 0.28125, 0.1875, 0.703125, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875]","[0.8, 0.35173503, 0.2, 0.2, 0.60299486, 0.2, 0.2, 0.2, 0.2, 0.3669072, 0.2, 0.30305824, 0.2, 0.70633006, 0.2, 0.8, 0.2, 0.2, 0.2]"
-42,17.56,-13.646086922103583,15444288,38.08664772727273,28.883136094674555,adc_episode_042,"[0.5625, 0.1875, 0.1875, 0.1875, 0.75, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.3125, 0.796875, 0.1875, 0.796875, 0.734375]","[0.6012403, 0.2, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.3211811, 0.8, 0.2, 0.8, 0.74478716]"
-43,71.38,-4.827325845719735,21146112,52.14772727272727,62.23440021516945,BEST_adc_episode_043,"[0.1875, 0.625, 0.1875, 0.25, 0.75, 0.1875, 0.25, 0.6875, 0.1875, 0.1875, 0.21875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.6491373, 0.2, 0.25361294, 0.8, 0.2, 0.304824, 0.7104509, 0.2, 0.2, 0.21978553, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-44,42.36,-9.643984552164621,18465408,45.53693181818182,35.00874125874126,adc_episode_044,"[0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875, 0.640625, 0.1875, 0.1875, 0.796875]","[0.2, 0.2, 0.2, 0.5725687, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.6529644, 0.2, 0.2, 0.8]"
-45,37.580000000000005,-10.406994684242859,17409600,42.93323863636363,42.3513986013986,adc_episode_045,"[0.1875, 0.1875, 0.625, 0.1875, 0.5625, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.59375, 0.1875, 0.578125, 0.796875, 0.1875, 0.1875, 0.1875, 0.265625]","[0.2, 0.2, 0.64694035, 0.2, 0.5751752, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.61393005, 0.2, 0.5800823, 0.8, 0.2, 0.2, 0.2, 0.2714306]"
-46,71.10000000000001,-4.9185175227823805,24620544,60.71590909090909,61.49475524475524,adc_episode_046,"[0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.3125, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.37394428, 0.2, 0.2, 0.2, 0.2, 0.4735648, 0.2, 0.2, 0.2, 0.2, 0.31454554, 0.2, 0.38250083, 0.2, 0.2, 0.2, 0.2, 0.2]"
-47,58.099999999999994,-7.135814142044416,24905088,61.41761363636363,55.68181818181818,adc_episode_047,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.24997778, 0.2, 0.2, 0.25642136, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.20208606, 0.2]"
-48,33.87,-10.624332326708572,9490752,23.404829545454543,15.556078536847767,adc_episode_048,"[0.1875, 0.75, 0.1875, 0.75, 0.75, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.1875, 0.25, 0.78125, 0.1875, 0.796875, 0.796875, 0.6875, 0.796875, 0.1875]","[0.2, 0.8, 0.2, 0.7972511, 0.8, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2638189, 0.8, 0.2, 0.8, 0.8, 0.68971115, 0.8, 0.2]"
-49,41.730000000000004,-9.687348185376328,16600320,40.9375,47.39107046799354,adc_episode_049,"[0.75, 0.5625, 0.6875, 0.75, 0.1875, 0.375, 0.1875, 0.78125, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.8, 0.5791302, 0.73390216, 0.8, 0.2, 0.42244545, 0.2, 0.8, 0.2, 0.25858313, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2]"
-50,29.439999999999998,-11.394023886660747,10303488,25.40909090909091,25.514389456697153,adc_episode_050,"[0.75, 0.1875, 0.75, 0.625, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.625, 0.78125, 0.78125, 0.1875, 0.1875, 0.796875, 0.1875, 0.796875, 0.1875]","[0.8, 0.2, 0.8, 0.63137066, 0.7262188, 0.2, 0.2, 0.2, 0.2, 0.8, 0.64289993, 0.8, 0.8, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2]"
-51,25.22,-12.155965641272227,11474496,28.296875,25.10758472296934,adc_episode_051,"[0.75, 0.1875, 0.75, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.375, 0.3125, 0.78125, 0.1875, 0.78125, 0.25, 0.796875, 0.1875, 0.1875, 0.796875, 0.796875]","[0.8, 0.2, 0.8, 0.2, 0.7343302, 0.2, 0.22157398, 0.2, 0.3919408, 0.33233124, 0.8, 0.2, 0.8, 0.25799406, 0.8, 0.2, 0.2, 0.8, 0.8]"
-52,52.910000000000004,-7.900466636025696,19334016,47.67897727272727,42.43881118881119,adc_episode_052,"[0.1875, 0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.65625]","[0.2, 0.4859042, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.8, 0.6583399]"
-53,62.92,-6.2543722101420105,21151872,52.16193181818182,57.295589026358265,adc_episode_053,"[0.75, 0.1875, 0.1875, 0.4375, 0.1875, 0.1875, 0.75, 0.1875, 0.40625, 0.1875, 0.1875, 0.1875, 0.1875, 0.515625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.8, 0.2, 0.2, 0.48288637, 0.2, 0.2, 0.8, 0.2, 0.42442933, 0.2, 0.2, 0.2, 0.2, 0.5255449, 0.2, 0.2, 0.2, 0.2, 0.2]"
-54,31.399999999999995,-11.382939465254529,16081920,39.659090909090914,54.45131791285638,adc_episode_054,"[0.1875, 0.1875, 0.1875, 0.5625, 0.375, 0.75, 0.75, 0.78125, 0.1875, 0.78125, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.21875, 0.1875, 0.1875]","[0.2, 0.2, 0.22435676, 0.5716205, 0.4226477, 0.8, 0.8, 0.8, 0.2, 0.8, 0.2, 0.79095227, 0.2, 0.2, 0.2, 0.2, 0.2190416, 0.2, 0.2]"
-55,34.63,-10.828071067897405,15623424,38.528409090909086,44.80567509413663,adc_episode_055,"[0.75, 0.75, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875]","[0.8, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2]"
-56,48.33,-8.67022334386866,19384704,47.80397727272727,47.63649811726735,adc_episode_056,"[0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.65625, 0.1875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875, 0.34375]","[0.8, 0.2, 0.2, 0.2, 0.23423581, 0.2, 0.2, 0.2, 0.8, 0.2, 0.6614329, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.35184875]"
-57,34.01,-10.956348117662815,16240896,40.05113636363637,47.048144163528775,adc_episode_057,"[0.75, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.21875, 0.1875, 0.21875, 0.1875, 0.609375]","[0.8, 0.2, 0.7306854, 0.2, 0.2, 0.2, 0.22854377, 0.8, 0.8, 0.2, 0.2, 0.8, 0.8, 0.2, 0.22898902, 0.2, 0.2226408, 0.2, 0.61708784]"
-58,31.86,-10.759572607396477,7205760,17.769886363636363,14.352474448628294,adc_episode_058,"[0.75, 0.75, 0.5625, 0.1875, 0.4375, 0.75, 0.1875, 0.78125, 0.1875, 0.78125, 0.1875, 0.78125, 0.25, 0.734375, 0.796875, 0.796875, 0.1875, 0.78125, 0.1875]","[0.8, 0.8, 0.62327933, 0.2, 0.43898144, 0.8, 0.2, 0.8, 0.2, 0.8, 0.2, 0.8, 0.27213866, 0.7497701, 0.8, 0.8, 0.2, 0.7962234, 0.2]"
-59,20.389999999999997,-13.12162168285559,14394816,35.49857954545455,34.08418504572351,adc_episode_059,"[0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.78125, 0.1875, 0.71875, 0.78125, 0.796875, 0.59375, 0.234375, 0.1875, 0.4375, 0.1875]","[0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.8, 0.2, 0.73897755, 0.8, 0.8, 0.59593153, 0.24767463, 0.2, 0.44092536, 0.2]"
-60,42.24,-9.649299142145253,17999424,44.387784090909086,40.39806347498655,adc_episode_060,"[0.625, 0.1875, 0.1875, 0.1875, 0.375, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.53125, 0.21875, 0.1875, 0.1875, 0.796875, 0.796875, 0.1875, 0.1875]","[0.6789254, 0.2, 0.2, 0.2, 0.41449782, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5474312, 0.23008972, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2]"
-61,25.580000000000002,-12.237406625544631,13848768,34.15198863636363,28.153577192038732,adc_episode_061,"[0.75, 0.75, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.78125, 0.1875, 0.40625, 0.1875, 0.1875, 0.1875, 0.796875, 0.796875, 0.1875, 0.1875, 0.796875, 0.1875]","[0.8, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.43456522, 0.2, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.8, 0.2]"
-62,66.46000000000001,-5.631874716371184,19609344,48.35795454545455,47.21288327057558,adc_episode_062,"[0.1875, 0.5, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.53125, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875]","[0.2, 0.5553538, 0.37946534, 0.2, 0.2, 0.2, 0.2, 0.2, 0.54943514, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2]"
-63,23.729999999999997,-12.400162928495543,11504448,28.370738636363633,24.889053254437872,adc_episode_063,"[0.1875, 0.1875, 0.75, 0.75, 0.75, 0.1875, 0.1875, 0.78125, 0.6875, 0.1875, 0.1875, 0.5, 0.78125, 0.1875, 0.796875, 0.796875, 0.1875, 0.1875, 0.796875]","[0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2, 0.8, 0.69820523, 0.2, 0.2, 0.5295152, 0.8, 0.2, 0.8, 0.8, 0.2, 0.2, 0.8]"
-64,41.93,-9.69554173349417,17828352,43.965909090909086,44.48292092522862,adc_episode_064,"[0.75, 0.75, 0.1875, 0.1875, 0.3125, 0.1875, 0.4375, 0.65625, 0.1875, 0.1875, 0.1875, 0.1875, 0.71875, 0.1875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875]","[0.8, 0.8, 0.2, 0.2, 0.31582698, 0.2, 0.44430465, 0.66639036, 0.2, 0.2, 0.2, 0.2, 0.7403013, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2]"
-65,27.639999999999997,-12.020705528401033,16392960,40.42613636363636,33.976600322754166,adc_episode_065,"[0.75, 0.75, 0.5625, 0.1875, 0.1875, 0.1875, 0.1875, 0.59375, 0.1875, 0.1875, 0.1875, 0.28125, 0.46875, 0.796875, 0.796875, 0.71875, 0.1875, 0.1875, 0.296875]","[0.8, 0.8, 0.5836422, 0.2, 0.2, 0.2, 0.2, 0.611194, 0.2, 0.2, 0.2, 0.30991438, 0.47990906, 0.8, 0.8, 0.723347, 0.2, 0.2, 0.30606496]"
-66,28.590000000000003,-11.700082851705352,13051008,32.18465909090909,22.34736417428725,adc_episode_066,"[0.1875, 0.1875, 0.25, 0.625, 0.625, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.78125, 0.1875, 0.59375, 0.59375, 0.203125, 0.796875, 0.796875, 0.1875]","[0.2, 0.2, 0.26739582, 0.677448, 0.63070637, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.8, 0.2, 0.60063773, 0.60283333, 0.20699446, 0.8, 0.8, 0.2]"
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-102,36.29,-10.5376987305926,15250176,37.60795454545455,42.11269499731038,adc_episode_102,"[0.1875, 0.1875, 0.625, 0.1875, 0.1875, 0.6875, 0.75, 0.1875, 0.1875, 0.75, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.1875, 0.796875]","[0.2, 0.2, 0.67869914, 0.2, 0.2, 0.7297528, 0.8, 0.2, 0.2, 0.7662581, 0.2, 0.8, 0.2, 0.2, 0.2, 0.57784486, 0.2, 0.2, 0.8]"
-103,41.48,-9.78557141516779,18288000,45.09943181818182,44.6947283485745,adc_episode_103,"[0.5, 0.5625, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.1875, 0.1875, 0.21875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.40625, 0.1875]","[0.51225, 0.56876767, 0.2, 0.2, 0.2, 0.5808971, 0.2, 0.2, 0.2, 0.24513495, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.41001064, 0.2]"
-104,33.41,-11.149230973441837,18682560,46.07244318181819,43.71974179666488,adc_episode_104,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.75, 0.78125, 0.25, 0.1875, 0.1875, 0.1875, 0.53125, 0.796875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.78324515, 0.8, 0.8, 0.2783852, 0.2, 0.2, 0.2, 0.53744537, 0.8, 0.2, 0.2, 0.2, 0.2, 0.8]"
-105,27.11,-12.167858180228798,17777664,43.84090909090909,40.142549757934376,adc_episode_105,"[0.75, 0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.1875, 0.1875, 0.625, 0.796875, 0.1875, 0.1875]","[0.8, 0.4961096, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.62965584, 0.8, 0.2, 0.2]"
-106,33.97,-10.878435111960195,14288832,35.23721590909091,26.586874663797737,adc_episode_106,"[0.1875, 0.1875, 0.1875, 0.1875, 0.625, 0.1875, 0.4375, 0.1875, 0.78125, 0.75, 0.1875, 0.1875, 0.78125, 0.796875, 0.1875, 0.484375, 0.1875, 0.796875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.6388182, 0.2, 0.46117774, 0.2, 0.8, 0.75399226, 0.2, 0.2, 0.8, 0.8, 0.2, 0.4907437, 0.2, 0.8, 0.2]"
-107,26.559999999999995,-12.172967054445206,15797952,38.95880681818182,29.716917697686927,adc_episode_107,"[0.75, 0.1875, 0.1875, 0.5, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.59375, 0.1875, 0.1875, 0.796875, 0.1875, 0.296875, 0.796875, 0.5625, 0.1875]","[0.8, 0.2, 0.2, 0.5223405, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.6135504, 0.2, 0.2, 0.8, 0.2, 0.3040818, 0.8, 0.5634349, 0.2]"
-108,77.38000000000001,-3.8692170034074578,26836992,66.18181818181819,66.04020979020979,BEST_adc_episode_108,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-109,81.33,-3.1935579776134944,26836992,66.18181818181819,66.04020979020979,BEST_adc_episode_109,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
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-111,81.04,-3.2431633238110265,26836992,66.18181818181819,66.04020979020979,adc_episode_111,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
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-114,27.01,-11.981601674823823,13462272,33.19886363636363,50.75981710597095,adc_episode_114,"[0.1875, 0.4375, 0.375, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.21875, 0.78125, 0.5, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.25]","[0.2, 0.49499094, 0.43129387, 0.8, 0.8, 0.8, 0.785184, 0.8, 0.2340342, 0.8, 0.5098725, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.25850096]"
-115,55.03,-7.566110482143765,20272896,49.99431818181818,56.79464766003227,adc_episode_115,"[0.1875, 0.1875, 0.6875, 0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.72124887, 0.49566802, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-116,26.839999999999996,-12.131262167295658,15899904,39.21022727272727,41.53778913394298,adc_episode_116,"[0.75, 0.75, 0.1875, 0.1875, 0.75, 0.1875, 0.625, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.625, 0.796875, 0.1875, 0.1875, 0.1875]","[0.8, 0.8, 0.2, 0.2, 0.8, 0.2, 0.6803284, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.6398739, 0.8, 0.2, 0.2, 0.2]"
-117,29.169999999999995,-11.678904640013842,14485248,35.721590909090914,40.44176976869285,adc_episode_117,"[0.1875, 0.1875, 0.1875, 0.75, 0.75, 0.75, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.6875, 0.46875, 0.796875, 0.1875, 0.1875, 0.1875, 0.359375, 0.515625]","[0.2, 0.2, 0.2, 0.8, 0.8, 0.79454744, 0.2, 0.2, 0.79575217, 0.2, 0.20624615, 0.7088859, 0.4934461, 0.8, 0.2, 0.2, 0.2, 0.37098703, 0.51572156]"
-118,57.99000000000001,-7.095227075283277,21625344,53.32954545454546,55.5506993006993,adc_episode_118,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.359375, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.22620188, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.36533162, 0.2, 0.2, 0.2]"
-119,36.27,-10.57366226733589,16051968,39.58522727272727,38.94566971490048,adc_episode_119,"[0.1875, 0.1875, 0.1875, 0.3125, 0.75, 0.75, 0.1875, 0.1875, 0.625, 0.78125, 0.1875, 0.1875, 0.1875, 0.40625, 0.1875, 0.34375, 0.1875, 0.796875, 0.1875]","[0.23939613, 0.2, 0.24166289, 0.3235759, 0.8, 0.8, 0.21458313, 0.2, 0.6409356, 0.8, 0.2, 0.2, 0.2, 0.40699226, 0.2, 0.3481135, 0.2, 0.8, 0.2]"
-120,40.1,-10.002224747039559,17862336,44.04971590909091,42.00847229693384,adc_episode_120,"[0.75, 0.75, 0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.578125, 0.796875, 0.1875, 0.1875, 0.421875]","[0.8, 0.8, 0.2, 0.2, 0.8, 0.2, 0.2, 0.3264543, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5856397, 0.8, 0.2, 0.2, 0.43354145]"
-121,63.12,-6.2030093047409185,20164608,49.72727272727273,57.23171059709521,adc_episode_121,"[0.75, 0.1875, 0.1875, 0.1875, 0.6875, 0.1875, 0.1875, 0.5625, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.34375, 0.1875, 0.1875, 0.1875]","[0.8, 0.2, 0.2, 0.2, 0.70151, 0.2, 0.2205477, 0.59006405, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3547539, 0.2, 0.2, 0.2]"
-122,61.3,-6.541390018403607,21917952,54.05113636363637,52.437466379774065,adc_episode_122,"[0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.78125, 0.78125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.28125, 0.1875, 0.1875, 0.1875, 0.796875]","[0.2, 0.2, 0.2, 0.28546548, 0.2, 0.2, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.28455177, 0.2, 0.2, 0.2, 0.8]"
-123,49.79,-8.426576379390394,19436544,47.93181818181818,53.153577192038725,adc_episode_123,"[0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.78125, 0.1875, 0.1875, 0.28125, 0.625, 0.1875, 0.1875, 0.1875, 0.1875, 0.359375, 0.1875]","[0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2813415, 0.6549368, 0.2, 0.2, 0.2, 0.2, 0.36523858, 0.2]"
-124,43.14,-9.449592561358115,16502976,40.69744318181818,38.99610005379236,adc_episode_124,"[0.625, 0.1875, 0.375, 0.3125, 0.1875, 0.1875, 0.625, 0.1875, 0.78125, 0.1875, 0.1875, 0.1875, 0.78125, 0.1875, 0.203125, 0.265625, 0.1875, 0.796875, 0.65625]","[0.6728497, 0.2, 0.38401634, 0.36815026, 0.2, 0.2, 0.6299578, 0.2, 0.8, 0.2, 0.2, 0.2, 0.8, 0.2, 0.21777517, 0.27146775, 0.2, 0.8, 0.66454387]"
-125,42.35,-9.691245955090697,19984896,49.284090909090914,29.77743410435718,adc_episode_125,"[0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.34375, 0.796875, 0.46875, 0.1875, 0.734375, 0.796875, 0.1875]","[0.38113716, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.37345263, 0.8, 0.48365656, 0.2, 0.7452191, 0.8, 0.2]"
-126,29.590000000000003,-11.530059177736678,12936960,31.90340909090909,36.417428725121034,adc_episode_126,"[0.75, 0.75, 0.1875, 0.25, 0.4375, 0.75, 0.1875, 0.1875, 0.78125, 0.1875, 0.5, 0.1875, 0.78125, 0.1875, 0.359375, 0.1875, 0.1875, 0.796875, 0.1875]","[0.8, 0.8, 0.2, 0.25673193, 0.4937408, 0.8, 0.2, 0.2, 0.8, 0.2, 0.5135013, 0.2, 0.8, 0.2, 0.36596966, 0.2, 0.2, 0.8, 0.2]"
-127,55.14,-7.531566170682959,19560960,48.23863636363636,62.79585798816568,adc_episode_127,"[0.5, 0.625, 0.1875, 0.75, 0.1875, 0.75, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.50116086, 0.62564194, 0.2, 0.8, 0.2, 0.8, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-128,67.8,-5.46562479769551,23534784,58.03835227272728,50.80016137708445,adc_episode_128,"[0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875, 0.1875, 0.265625, 0.328125, 0.1875]","[0.55031604, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2781719, 0.3289384, 0.2]"
-129,34.28,-10.981876024302355,18076032,44.57670454545454,32.60489510489511,adc_episode_129,"[0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.40625, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.796875, 0.796875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.4095253, 0.2, 0.37783107, 0.2, 0.2, 0.2, 0.2, 0.8, 0.8, 0.8, 0.2, 0.2]"
-130,24.490000000000002,-12.603046093431328,17726400,43.71448863636363,36.48466917697687,adc_episode_130,"[0.25, 0.1875, 0.1875, 0.1875, 0.6875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.625, 0.1875, 0.78125, 0.796875, 0.1875, 0.796875, 0.1875, 0.1875, 0.1875]","[0.29988107, 0.2, 0.2, 0.2, 0.7353318, 0.2, 0.2, 0.2, 0.20912999, 0.2, 0.64731693, 0.2, 0.8, 0.8, 0.2, 0.8, 0.2, 0.2, 0.2]"
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-965,79.23,-3.549968613385738,26477568,65.29545454545455,63.942307692307686,adc_episode_965,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.28125, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2910293, 0.2, 0.2]"
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-980,76.31,-4.03222155111114,24662016,60.81818181818181,65.24677245831091,adc_episode_980,"[0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.50366855, 0.2, 0.2, 0.2, 0.2, 0.5673541, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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-982,82.26,-3.029536940216723,26099712,64.36363636363637,65.77124798278643,BEST_adc_episode_982,"[0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.5285309, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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-985,77.44,-3.837854349644866,24440832,60.27272727272728,64.11713286713287,adc_episode_985,"[0.5625, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.62334764, 0.39141592, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.44242856, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-986,77.13,-3.8928220606310195,24680448,60.86363636363637,65.2534965034965,adc_episode_986,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-987,67.34,-5.530234107863371,22583808,55.69318181818181,59.83391608391608,adc_episode_987,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.625, 0.1875, 0.40625, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.6507755, 0.2, 0.41536292, 0.2, 0.7578364, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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-994,78.82000000000001,-3.619084482133557,26357760,65.0,64.5541958041958,adc_episode_994,"[0.1875, 0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2843609, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.25292957, 0.2, 0.2, 0.2]"
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-996,78.78,-3.629742918315926,26836992,66.18181818181819,66.04020979020979,adc_episode_996,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.20234685, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-997,73.99,-4.430123344077626,24950016,61.528409090909086,59.484265734265726,adc_episode_997,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.71875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.375]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.72585577, 0.2, 0.2, 0.2, 0.2, 0.2, 0.38931537]"
-998,73.36,-4.503497769579458,21966336,54.17045454545455,49.51250672404519,adc_episode_998,"[0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.65625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.796875, 0.1875]","[0.5574589, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.672683, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.8, 0.2]"
-999,73.42,-4.516463793826047,23961600,59.09090909090909,62.10664335664335,adc_episode_999,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.5625, 0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.5101594, 0.2, 0.2, 0.2, 0.2, 0.5640174, 0.2, 0.2, 0.2, 0.26417804, 0.2, 0.2, 0.2, 0.2]"
-1000,74.44999999999999,-4.346506145098685,24440832,60.27272727272728,52.0541958041958,adc_episode_1000,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.703125, 0.1875, 0.1875, 0.1875, 0.40625]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.7138068, 0.2, 0.2, 0.2, 0.41154912]"
-1001,81.87,-3.096266927617674,26118144,64.4090909090909,65.77797202797203,adc_episode_1001,"[0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.4037892, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1002,76.81,-3.9407259384795625,23991552,59.16477272727273,61.23251748251748,adc_episode_1002,"[0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.546875]","[0.2, 0.2, 0.2, 0.7900965, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.55849725]"
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-1004,78.9,-3.6092165681652237,26836992,66.18181818181819,66.04020979020979,adc_episode_1004,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-1005,66.72,-5.692641108461548,26836992,66.18181818181819,66.04020979020979,adc_episode_1005,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-1006,77.86,-3.7871116028046488,26836992,66.18181818181819,66.04020979020979,adc_episode_1006,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-1007,76.4,-4.036848862971531,26836992,66.18181818181819,66.04020979020979,adc_episode_1007,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-1008,80.5,-3.3355318994891876,26836992,66.18181818181819,66.04020979020979,adc_episode_1008,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
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-1682,79.89,-3.4398741794219263,26836992,66.18181818181819,66.04020979020979,adc_episode_1682,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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-1699,79.19,-3.559611221967694,26836992,66.18181818181819,66.04020979020979,adc_episode_1699,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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-1701,78.86,-3.616058684882126,26836992,66.18181818181819,66.04020979020979,adc_episode_1701,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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-1704,78.42,-3.691321968768035,26836992,66.18181818181819,66.04020979020979,adc_episode_1704,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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-1908,79.10000000000001,-3.5654609468825833,25638912,63.227272727272734,65.60314685314685,adc_episode_1908,"[0.1875, 0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.5487588, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1909,78.94,-3.602374451448324,26836992,66.18181818181819,66.04020979020979,adc_episode_1909,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1910,71.59,-4.831121272880711,24276096,59.866477272727266,62.89335664335665,adc_episode_1910,"[0.1875, 0.75, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.328125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.8, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.34153813, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1911,76.24,-4.064217329839135,26836992,66.18181818181819,66.04020979020979,adc_episode_1911,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1912,74.99,-4.278033477242289,26836992,66.18181818181819,66.04020979020979,adc_episode_1912,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1913,79.71000000000001,-3.4670077498814873,26357760,65.0,65.86538461538461,adc_episode_1913,"[0.1875, 0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.36224183, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1914,80.06,-3.410795183375099,26836992,66.18181818181819,66.04020979020979,adc_episode_1914,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1915,80.93,-3.2619791447825035,26836992,66.18181818181819,66.04020979020979,adc_episode_1915,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1916,79.27,-3.5445341501473266,26657280,65.73863636363636,65.51573426573427,adc_episode_1916,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.26739526, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1917,70.42999999999999,-5.055382747255089,26597376,65.5909090909091,65.9527972027972,adc_episode_1917,"[0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.29205728, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1918,79.2,-3.5529676552520653,26208000,64.63068181818183,65.12237762237763,adc_episode_1918,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.40625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.40669847, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1919,77.86999999999999,-3.784658870582041,26747136,65.96022727272727,65.9090909090909,adc_episode_1919,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.21875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.22541955, 0.2, 0.2, 0.2, 0.2, 0.2310986, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1920,77.85,-3.7888221319838737,26836992,66.18181818181819,66.04020979020979,adc_episode_1920,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1921,79.77,-3.4604005295726306,26836992,66.18181818181819,66.04020979020979,adc_episode_1921,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.20453267, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1922,79.14999999999999,-3.5643484413908704,26567424,65.51704545454545,64.46678321678321,adc_episode_1922,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.28125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2870416, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1923,74.37,-4.384086286354251,26836992,66.18181818181819,66.04020979020979,adc_episode_1923,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1924,77.12,-3.908606108370973,26247168,64.72727272727272,65.82504034427112,adc_episode_1924,"[0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.49654052, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1925,78.08,-3.739784031370774,25675776,63.31818181818182,65.61659494351802,adc_episode_1925,"[0.1875, 0.1875, 0.1875, 0.1875, 0.4375, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.49523664, 0.310638, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1926,78.64,-3.6532130081906984,26777088,66.03409090909092,65.69055944055944,adc_episode_1926,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.203125, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.21852866, 0.2, 0.2, 0.2]"
-1927,77.4,-3.861723762805403,26357760,65.0,65.86538461538461,adc_episode_1927,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.3159471, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1928,80.11,-3.4022425374789718,26836992,66.18181818181819,66.04020979020979,adc_episode_1928,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1929,77.11,-3.9123149523543352,26477568,65.29545454545455,63.942307692307686,adc_episode_1929,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.28125, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.29221505, 0.2, 0.2, 0.2, 0.2]"
-1930,69.69000000000001,-5.184613942231655,26836992,66.18181818181819,66.04020979020979,adc_episode_1930,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1931,79.05,-3.5824043575844486,26689536,65.81818181818181,65.98641742872512,adc_episode_1931,"[0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2578239, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1932,77.36000000000001,-3.855189201564723,24846336,61.272727272727266,61.380446476600326,adc_episode_1932,"[0.625, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.375, 0.1875]","[0.6868233, 0.2, 0.31244382, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3883304, 0.2]"
-1933,80.53,-3.3304003119515126,26836992,66.18181818181819,66.04020979020979,adc_episode_1933,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1934,80.44,-3.3457950745645397,26836992,66.18181818181819,66.04020979020979,adc_episode_1934,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1935,78.75,-3.6310455734342844,26357760,65.0,65.86538461538461,adc_episode_1935,"[0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.33433303, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1936,80.31,-3.368031953894467,26836992,66.18181818181819,66.04020979020979,adc_episode_1936,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1937,78.8,-3.626321859957478,26836992,66.18181818181819,66.04020979020979,adc_episode_1937,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1938,78.37,-3.6998746146641603,26836992,66.18181818181819,66.04020979020979,adc_episode_1938,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1939,77.92,-3.7708534893435344,26118144,64.4090909090909,65.77797202797203,adc_episode_1939,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.42202315, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1940,78.94,-3.602374451448324,26836992,66.18181818181819,66.04020979020979,adc_episode_1940,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1941,79.95,-3.429611004346576,26836992,66.18181818181819,66.04020979020979,adc_episode_1941,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1942,71.63,-4.850226869787855,26597376,65.5909090909091,65.69055944055944,adc_episode_1942,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.25605658, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1943,77.34,-3.876059120124358,26836992,66.18181818181819,66.04020979020979,adc_episode_1943,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1944,79.77,-3.4604005295726306,26836992,66.18181818181819,66.04020979020979,adc_episode_1944,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1945,77.27999999999999,-3.878191009895834,25893504,63.85511363636363,60.53321678321678,adc_episode_1945,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.515625, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.5297329, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1946,77.8,-3.7909650487437943,26073216,64.29829545454545,61.58216783216783,adc_episode_1946,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.453125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.46807814, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1947,80.87,-3.272242319857854,26836992,66.18181818181819,66.04020979020979,adc_episode_1947,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1948,81.61,-3.1450463908722863,26747136,65.96022727272727,65.9090909090909,adc_episode_1948,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.21875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2435019, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1949,77.35,-3.874348590945135,26836992,66.18181818181819,66.04020979020979,adc_episode_1949,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.22736056, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1950,79.44,-3.5168479924870626,26836992,66.18181818181819,66.04020979020979,adc_episode_1950,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1951,78.09,-3.747769431682467,26836992,66.18181818181819,66.04020979020979,adc_episode_1951,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1952,79.39,-3.5254006383831875,26836992,66.18181818181819,66.04020979020979,adc_episode_1952,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1953,77.23,-3.8948749410958374,26836992,66.18181818181819,66.04020979020979,adc_episode_1953,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1954,80.15,-3.3947346850905316,26747136,65.96022727272727,65.9090909090909,adc_episode_1954,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.21875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.22971277, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1955,80.27,-3.3748740706113693,26836992,66.18181818181819,66.04020979020979,adc_episode_1955,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1956,78.22,-3.7255325523525396,26836992,66.18181818181819,66.04020979020979,adc_episode_1956,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1957,78.35,-3.7032956730226125,26836992,66.18181818181819,66.04020979020979,adc_episode_1957,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1958,78.27,-3.7169799064564146,26836992,66.18181818181819,66.04020979020979,adc_episode_1958,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1959,76.41,-4.035138333792306,26836992,66.18181818181819,66.04020979020979,adc_episode_1959,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1960,81.84,-3.106320989473008,26836992,66.18181818181819,66.04020979020979,adc_episode_1960,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1961,79.82000000000001,-3.4518478836765034,26836992,66.18181818181819,66.04020979020979,adc_episode_1961,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1962,77.85,-3.787601743221744,26689536,65.81818181818181,65.98641742872512,adc_episode_1962,"[0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.27974877, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1963,79.96,-3.427900475167351,26836992,66.18181818181819,66.04020979020979,adc_episode_1963,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1964,78.31,-3.7101377897395125,26836992,66.18181818181819,66.04020979020979,adc_episode_1964,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1965,78.55,-3.669085089438108,26836992,66.18181818181819,66.04020979020979,adc_episode_1965,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1966,76.6,-4.002638279387028,26836992,66.18181818181819,66.04020979020979,adc_episode_1966,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1967,72.74000000000001,-4.662902542567962,26836992,66.18181818181819,66.04020979020979,adc_episode_1967,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1968,79.01,-3.5904007471937467,26836992,66.18181818181819,66.04020979020979,adc_episode_1968,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1969,79.02,-3.5886902180145217,26836992,66.18181818181819,66.04020979020979,adc_episode_1969,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1970,74.58,-4.341263392169957,26118144,64.4090909090909,64.99125874125873,adc_episode_1970,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.4375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.43947646, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1971,80.91000000000001,-3.2654002031409517,26836992,66.18181818181819,66.04020979020979,adc_episode_1971,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1972,74.42999999999999,-4.3738231112789006,26836992,66.18181818181819,66.04020979020979,adc_episode_1972,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1973,79.81,-3.4535584128557284,26836992,66.18181818181819,66.04020979020979,adc_episode_1973,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1974,80.13,-3.3988214791205236,26836992,66.18181818181819,66.04020979020979,adc_episode_1974,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.20435856, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1975,80.56,-3.3252687244138377,26836992,66.18181818181819,66.04020979020979,adc_episode_1975,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1976,76.64999999999999,-3.983421679890352,25638912,63.227272727272734,65.60314685314685,adc_episode_1976,"[0.1875, 0.1875, 0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.543772, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1977,80.77,-3.289347611650108,26836992,66.18181818181819,66.04020979020979,adc_episode_1977,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1978,78.39,-3.6964535563057104,26836992,66.18181818181819,66.04020979020979,adc_episode_1978,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1979,81.88,-3.099478872756108,26836992,66.18181818181819,66.04020979020979,adc_episode_1979,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1980,77.34,-3.876059120124358,26836992,66.18181818181819,66.04020979020979,adc_episode_1980,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1981,78.45,-3.6852261738052547,26717184,65.88636363636363,65.86538461538461,adc_episode_1981,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.21875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.22596191, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1982,77.27999999999999,-3.886322295199714,26836992,66.18181818181819,66.04020979020979,adc_episode_1982,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1983,78.95,-3.594948639070716,26118144,64.4090909090909,65.77797202797203,adc_episode_1983,"[0.1875, 0.1875, 0.3125, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.3152942, 0.2, 0.2, 0.26687378, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1984,79.94,-3.425875045119171,26118144,64.4090909090909,65.77797202797203,adc_episode_1984,"[0.1875, 0.1875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.41516373, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1985,78.61,-3.658821914362756,26836992,66.18181818181819,66.04020979020979,adc_episode_1985,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1986,79.08,-3.5746575715880113,26357760,65.0,65.86538461538461,adc_episode_1986,"[0.1875, 0.3125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.36777458, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1987,78.9,-3.6092165681652237,26836992,66.18181818181819,66.04020979020979,adc_episode_1987,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1988,77.57000000000001,-3.8367169490021786,26836992,66.18181818181819,66.04020979020979,adc_episode_1988,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1989,79.25,-3.549348046892342,26836992,66.18181818181819,66.04020979020979,adc_episode_1989,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1990,80.67999999999999,-3.2919193613943794,25113600,61.93181818181818,64.1003227541689,adc_episode_1990,"[0.75, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.25, 0.1875]","[0.77393097, 0.29974255, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.26385593, 0.2]"
-1991,77.25,-3.8914538827373875,26836992,66.18181818181819,66.04020979020979,adc_episode_1991,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1992,78.71000000000001,-3.6417166225705015,26836992,66.18181818181819,66.04020979020979,adc_episode_1992,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1993,79.14,-3.5681638678638192,26836992,66.18181818181819,66.04020979020979,adc_episode_1993,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1994,82.17,-3.049873526558576,26836992,66.18181818181819,66.04020979020979,adc_episode_1994,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1995,78.19,-3.7306641398902167,26836992,66.18181818181819,66.04020979020979,adc_episode_1995,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1996,81.21000000000001,-3.214084327764197,26836992,66.18181818181819,66.04020979020979,adc_episode_1996,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1997,79.12,-3.571584926222269,26836992,66.18181818181819,66.04020979020979,adc_episode_1997,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1998,73.03,-4.595569894525298,25129728,61.971590909090914,64.33566433566433,adc_episode_1998,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.375, 0.1875, 0.53125, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.3947097, 0.2, 0.54585755, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-1999,79.47999999999999,-3.510005875770162,26836992,66.18181818181819,66.04020979020979,adc_episode_1999,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2000,80.06,-3.4090068305805366,26597376,65.5909090909091,65.9527972027972,adc_episode_2000,"[0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2536145, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2001,76.1,-4.080025472793703,25938432,63.96590909090909,64.72902097902097,adc_episode_2001,"[0.1875, 0.1875, 0.1875, 0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.234375, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.37674996, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.23849885, 0.2, 0.2, 0.2, 0.2]"
-2002,78.2,-3.72895361071099,26836992,66.18181818181819,66.04020979020979,adc_episode_2002,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2003,78.85,-3.608110001271132,25638912,63.227272727272734,65.60314685314685,adc_episode_2003,"[0.1875, 0.1875, 0.1875, 0.1875, 0.5, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.56121224, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2004,79.65,-3.480926879723331,26836992,66.18181818181819,66.04020979020979,adc_episode_2004,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2005,79.31,-3.53908487181699,26836992,66.18181818181819,66.04020979020979,adc_episode_2005,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2006,77.56,-3.8384274781814036,26836992,66.18181818181819,66.04020979020979,adc_episode_2006,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2007,72.7,-4.669744659284865,26836992,66.18181818181819,66.04020979020979,adc_episode_2007,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2008,78.79,-3.6280323891367012,26836992,66.18181818181819,66.04020979020979,adc_episode_2008,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2009,81.72,-3.1268473396237106,26836992,66.18181818181819,66.04020979020979,adc_episode_2009,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2010,79.02,-3.5886902180145217,26836992,66.18181818181819,66.04020979020979,adc_episode_2010,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2011,79.86999999999999,-3.4432952377803803,26836992,66.18181818181819,66.04020979020979,adc_episode_2011,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2012,80.45,-3.3440845453853147,26836992,66.18181818181819,66.04020979020979,adc_episode_2012,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-2013,78.9,-3.6092165681652237,26836992,66.18181818181819,66.04020979020979,adc_episode_2013,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-2014,78.03,-3.758032606757819,26836992,66.18181818181819,66.04020979020979,adc_episode_2014,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-2015,78.16,-3.7357957274278917,26836992,66.18181818181819,66.04020979020979,adc_episode_2015,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-2016,80.47,-3.3406634870268648,26836992,66.18181818181819,66.04020979020979,adc_episode_2016,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224, 0.20000000298023224]"
-2017,77.64999999999999,-3.8230327155683783,26836992,66.18181818181819,66.04020979020979,adc_episode_2017,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2018,79.28999999999999,-3.540648518622012,26597376,65.5909090909091,65.9527972027972,adc_episode_2018,"[0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.28459054, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2019,77.57000000000001,-3.8367169490021786,26836992,66.18181818181819,66.04020979020979,adc_episode_2019,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2020,77.71000000000001,-3.812769540493024,26836992,66.18181818181819,66.04020979020979,adc_episode_2020,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2021,78.37,-3.6998746146641603,26836992,66.18181818181819,66.04020979020979,adc_episode_2021,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2022,79.34,-3.5325651491579237,26657280,65.73863636363636,65.77797202797203,adc_episode_2022,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.25, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2648166, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2023,79.60000000000001,-3.489479525619458,26836992,66.18181818181819,66.04020979020979,adc_episode_2023,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2024,79.96,-3.427900475167351,26836992,66.18181818181819,66.04020979020979,adc_episode_2024,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2025,78.22,-3.722595878649079,26477568,65.29545454545455,63.942307692307686,adc_episode_2025,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.28125, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2846278, 0.2, 0.2, 0.2]"
-2026,77.34,-3.876059120124358,26836992,66.18181818181819,66.04020979020979,adc_episode_2026,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2027,78.82000000000001,-3.622900801599024,26836992,66.18181818181819,66.04020979020979,adc_episode_2027,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2028,79.19,-3.559611221967694,26836992,66.18181818181819,66.04020979020979,adc_episode_2028,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2029,79.77,-3.4604005295726306,26836992,66.18181818181819,66.04020979020979,adc_episode_2029,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2030,81.91,-3.094347285218433,26836992,66.18181818181819,66.04020979020979,adc_episode_2030,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2031,81.17999999999999,-3.219215915301874,26836992,66.18181818181819,66.04020979020979,adc_episode_2031,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2032,79.59,-3.491190054798683,26836992,66.18181818181819,66.04020979020979,adc_episode_2032,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2033,78.56,-3.6673745602588808,26836992,66.18181818181819,66.04020979020979,adc_episode_2033,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2034,77.69,-3.816190598851478,26836992,66.18181818181819,66.04020979020979,adc_episode_2034,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2035,78.9,-3.6092165681652237,26836992,66.18181818181819,66.04020979020979,adc_episode_2035,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2036,78.64999999999999,-3.6519797976458572,26836992,66.18181818181819,66.04020979020979,adc_episode_2036,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2037,80.71000000000001,-3.299610786725458,26836992,66.18181818181819,66.04020979020979,adc_episode_2037,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2038,79.96,-3.427900475167351,26836992,66.18181818181819,66.04020979020979,adc_episode_2038,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-2039,70.33,-5.050285550455721,24680448,60.86363636363637,53.4527972027972,adc_episode_2039,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.75, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.76229775, 0.2, 0.2]"
-2040,78.17,-3.73064733192252,26417664,65.14772727272728,63.59265734265735,adc_episode_2040,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.296875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.30741128, 0.2, 0.2]"
-2041,79.18,-3.561321751146917,26836992,66.18181818181819,66.04020979020979,adc_episode_2041,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
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-3140,77.82,-3.7939537195215505,26836992,66.18181818181819,66.04020979020979,adc_episode_3140,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3141,78.24,-3.7221114939940896,26836992,66.18181818181819,66.04020979020979,adc_episode_3141,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3142,81.52000000000001,-3.161057923208213,26836992,66.18181818181819,66.04020979020979,adc_episode_3142,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3143,78.88,-3.612637626523676,26836992,66.18181818181819,66.04020979020979,adc_episode_3143,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3144,80.77,-3.289347611650108,26836992,66.18181818181819,66.04020979020979,adc_episode_3144,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3145,80.25,-3.3782951289698193,26836992,66.18181818181819,66.04020979020979,adc_episode_3145,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3146,80.5,-3.3355318994891876,26836992,66.18181818181819,66.04020979020979,adc_episode_3146,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3147,79.44,-3.5168479924870626,26836992,66.18181818181819,66.04020979020979,adc_episode_3147,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3148,80.1,-3.4006455112922267,26394624,65.0909090909091,65.87883270575578,adc_episode_3148,"[0.375, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.42379773, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3149,80.28999999999999,-3.3714530122529194,26836992,66.18181818181819,66.04020979020979,adc_episode_3149,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3150,78.47999999999999,-3.681058793692685,26836992,66.18181818181819,66.04020979020979,adc_episode_3150,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3151,78.14,-3.739216785786342,26836992,66.18181818181819,66.04020979020979,adc_episode_3151,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
-3152,78.34,-3.7050062022018375,26836992,66.18181818181819,66.04020979020979,adc_episode_3152,"[0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875, 0.1875]","[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]"
diff --git a/examples/automated_deep_compression/sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv b/examples/automated_deep_compression/sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv
deleted file mode 100644
index dc8d010..0000000
--- a/examples/automated_deep_compression/sample_logs/clipped_ppo/macs_constrained_clipped-ppo.amc.csv
+++ /dev/null
@@ -1,3550 +0,0 @@
-episode,top1,reward,total_macs,normalized_macs,normalized_nnz,ckpt_name,action_history,agent_action_history
-1,33.040000000000006,0.3304000000000001,12934656,31.897727272727273,17.079074771382462,BEST_adc_episode_001,"[0.75, 0, 0, 0.75, 0.75, 0.1875, 0, 0, 0.78125, 0, 0.78125, 0, 0, 0.796875, 0.796875, 0.796875, 0.765625, 0.796875, 0]","[0.8, 0.0, 0.0, 0.75428534, 0.8, 0.24526766, 0.0, 0.0, 0.8, 0.0, 0.8, 0.0, 0.0, 0.8, 0.8, 0.8, 0.76642257, 0.8, 0.0]"
-2,34.51,0.34509999999999996,20291328,50.03977272727273,35.328133405056484,BEST_adc_episode_002,"[0.75, 0.125, 0, 0, 0.75, 0.5625, 0, 0, 0, 0.5625, 0, 0, 0, 0.796875, 0.796875, 0.796875, 0, 0.296875, 0.71875]","[0.8, 0.17893769, 0.0, 0.0, 0.8, 0.6087111, 0.0, 0.0, 0.0, 0.5730491, 0.0, 0.0, 0.0, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0]"
-3,46.79,0.4679,20015424,49.359375,43.854222700376546,BEST_adc_episode_003,"[0.25, 0.4375, 0.625, 0, 0, 0, 0, 0, 0.78125, 0, 0.78125, 0.40625, 0, 0, 0, 0.53125, 0.578125, 0.796875, 0]","[0.297757, 0.45267355, 0.6577741, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.8, 0.42538527, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0]"
-4,29.569999999999997,0.29569999999999996,20286144,50.02698863636363,39.3928187197418,adc_episode_004,"[0.75, 0, 0, 0, 0.75, 0.75, 0.3125, 0.3125, 0, 0, 0.375, 0, 0, 0, 0.796875, 0.640625, 0, 0.6875, 0.375]","[0.8, 0.0, 0.0, 0.0, 0.8, 0.8, 0.3202856, 0.3392121, 0.0, 0.0, 0.39771163, 0.0, 0.0, 0.0, 0.8, 0.6530791, 0.0, 0.4586793, 0.19104968]"
-5,41.7,0.41700000000000004,19999872,49.32102272727273,57.278778913394305,adc_episode_005,"[0.375, 0.75, 0, 0, 0.75, 0, 0.75, 0, 0, 0, 0.78125, 0.53125, 0, 0.390625, 0.71875, 0, 0.234375, 0, 0]","[0.4247698, 0.7967756, 0.0, 0.0, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.8, 0.55605996, 0.017714858, 0.4015374, 0.7275591, 0.0, 0.23993926, 0.0, 0.0]"
-6,42.39000000000001,0.42390000000000005,20293056,50.04403409090909,29.014254975793435,adc_episode_006,"[0, 0, 0.125, 0, 0, 0.5625, 0, 0, 0.4375, 0, 0.34375, 0.78125, 0, 0.71875, 0.796875, 0.796875, 0, 0.5, 0.59375]","[0.0, 0.0040054712, 0.13979279, 0.0, 0.0, 0.5806573, 0.0, 0.0, 0.4588497, 0.0, 0.0, 0.8, 0.0, 0.66444147, 0.8, 0.8, 0.0, 0.0, 0.37614235]"
-7,30.479999999999997,0.30479999999999996,15082560,37.19460227272727,42.07235072619688,adc_episode_007,"[0, 0.75, 0, 0, 0.5625, 0, 0.75, 0, 0.59375, 0.78125, 0.78125, 0.78125, 0, 0.046875, 0.609375, 0, 0, 0.796875, 0]","[0.0, 0.8, 0.020280894, 0.0, 0.60788745, 0.0, 0.8, 0.0, 0.60573864, 0.8, 0.8, 0.8, 0.0, 0.051320527, 0.62208414, 0.0, 0.0, 0.8, 0.0]"
-8,53.06999999999999,0.5307,20285568,50.02556818181818,37.14026358257128,BEST_adc_episode_008,"[0, 0, 0.75, 0, 0.1875, 0, 0.4375, 0.78125, 0, 0, 0.0625, 0.5625, 0.09375, 0.515625, 0, 0.609375, 0.484375, 0.609375, 0.484375]","[0.0, 0.0, 0.7530135, 0.0, 0.23333932, 0.0, 0.49174312, 0.8, 0.0, 0.0, 0.0823767, 0.56932735, 0.10851926, 0.52784395, 0.0, 0.47908375, 0.0, 0.0, 0.0]"
-9,41.76,0.41759999999999997,19842048,48.93181818181818,63.39766003227542,adc_episode_009,"[0.1875, 0.375, 0, 0.75, 0, 0, 0.75, 0, 0.78125, 0.75, 0.46875, 0, 0.78125, 0, 0.109375, 0, 0, 0, 0.796875]","[0.24605331, 0.43500635, 0.0, 0.8, 0.0, 0.0, 0.8, 0.0, 0.8, 0.75561655, 0.47159702, 0.0, 0.8, 0.0, 0.12234522, 0.0, 0.0, 0.0, 0.8]"
-10,52.910000000000004,0.5291,20281536,50.015625,42.16984938138784,adc_episode_010,"[0, 0.4375, 0.75, 0.75, 0, 0.75, 0, 0, 0, 0, 0.125, 0, 0, 0, 0.65625, 0.53125, 0.484375, 0.609375, 0.5]","[0.0, 0.47587755, 0.78828245, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.15506224, 0.0, 0.0, 0.0, 0.0, 0.5423126, 0.20657629, 0.0, 0.0]"
-11,49.21,0.4921,20191104,49.79261363636363,44.54679935449166,adc_episode_011,"[0.75, 0, 0, 0, 0.3125, 0.6875, 0, 0.40625, 0.78125, 0, 0.46875, 0, 0, 0, 0, 0.71875, 0.796875, 0, 0.703125]","[0.8, 0.0, 0.0, 0.0, 0.3607101, 0.7197027, 0.0, 0.42547822, 0.8, 0.0, 0.4968741, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.71647394]"
-12,34.27,0.3427,17372160,42.84090909090909,40.680473372781066,adc_episode_012,"[0.75, 0, 0, 0.75, 0.5625, 0.75, 0.75, 0, 0.6875, 0, 0, 0, 0.15625, 0.59375, 0.796875, 0, 0.703125, 0, 0.109375]","[0.8, 0.0, 0.0, 0.8, 0.59435767, 0.8, 0.8, 0.0, 0.7114255, 0.0, 0.0, 0.0, 0.16473037, 0.59655124, 0.8, 0.0, 0.7059997, 0.0, 0.12013451]"
-13,29.520000000000003,0.2952,16815744,41.46875,27.054195804195803,adc_episode_013,"[0, 0.625, 0.6875, 0.75, 0, 0.1875, 0.0625, 0, 0, 0, 0.78125, 0, 0.25, 0.796875, 0.609375, 0.796875, 0, 0.625, 0.796875]","[0.0, 0.6724296, 0.73265064, 0.8, 0.0, 0.21836385, 0.06630842, 0.0, 0.0, 0.0, 0.8, 0.0, 0.26587182, 0.8, 0.61081934, 0.8, 0.0, 0.6345352, 0.8]"
-14,29.390000000000004,0.29390000000000005,20030976,49.39772727272727,29.915277030661645,adc_episode_014,"[0.625, 0.75, 0, 0, 0, 0, 0.75, 0, 0, 0, 0.21875, 0.0625, 0.78125, 0.796875, 0.796875, 0, 0.578125, 0.78125, 0.078125]","[0.6552964, 0.8, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.24509926, 0.0, 0.8, 0.8, 0.8, 0.0, 0.47501427, 0.7823658, 0.08368976]"
-15,36.33,0.36329999999999996,19930752,49.15056818181818,32.35610543302851,adc_episode_015,"[0, 0, 0, 0, 0, 0, 0.625, 0.6875, 0, 0.78125, 0, 0.78125, 0.3125, 0, 0.796875, 0.796875, 0.046875, 0.25, 0.71875]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6532655, 0.6914734, 0.0, 0.8, 0.0, 0.8, 0.33062014, 0.0, 0.8, 0.8, 0.04796628, 0.25197905, 0.7241825]"
-16,49.22,0.49219999999999997,20277504,50.00568181818181,51.61713286713287,adc_episode_016,"[0.75, 0.75, 0.75, 0, 0, 0, 0, 0, 0.78125, 0.15625, 0, 0.78125, 0, 0.703125, 0.40625, 0.171875, 0, 0, 0.65625]","[0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.17285195, 0.0, 0.8, 0.0, 0.7038995, 0.41245338, 0.17942308, 0.0, 0.0, 0.0]"
-17,40.690000000000005,0.40690000000000004,20294784,50.04829545454545,53.67469069392147,adc_episode_017,"[0.1875, 0, 0, 0.75, 0, 0.75, 0.75, 0.78125, 0, 0.09375, 0.25, 0, 0.78125, 0, 0, 0.328125, 0.21875, 0.34375, 0.6875]","[0.22067475, 0.0, 0.0, 0.8, 0.0, 0.8, 0.8, 0.8, 0.0, 0.117871515, 0.26088646, 0.0, 0.79558635, 0.0, 0.0, 0.3374775, 0.22178704, 0.085413724, 0.0]"
-18,15.969999999999995,0.15969999999999995,13828032,34.10085227272727,22.676842388380848,adc_episode_018,"[0, 0.75, 0.5625, 0, 0, 0.75, 0.375, 0, 0.53125, 0, 0, 0.6875, 0.78125, 0.515625, 0.796875, 0, 0.765625, 0.796875, 0]","[0.0, 0.8, 0.61067885, 0.0, 0.0, 0.8, 0.39834756, 0.0, 0.5358859, 0.0, 0.0, 0.6956341, 0.8, 0.5284818, 0.8, 0.0, 0.7692603, 0.8, 0.0]"
-19,38.260000000000005,0.38260000000000005,17869824,44.06818181818182,52.400484131253364,adc_episode_019,"[0.75, 0.4375, 0.375, 0.75, 0, 0.75, 0, 0, 0.75, 0, 0, 0, 0.78125, 0.5625, 0.421875, 0, 0.25, 0, 0.46875]","[0.8, 0.49007672, 0.42494112, 0.8, 0.0, 0.8, 0.006823744, 0.0, 0.76080155, 0.0, 0.0, 0.0, 0.8, 0.57738996, 0.4251906, 0.0, 0.2599519, 0.0, 0.476169]"
-20,30.86,0.3086,17819712,43.94460227272727,38.11861215707369,adc_episode_020,"[0, 0.4375, 0.1875, 0, 0.75, 0, 0.125, 0.78125, 0.4375, 0, 0, 0.78125, 0.375, 0.5625, 0, 0, 0.796875, 0.796875, 0]","[0.0, 0.44244647, 0.24725108, 0.0, 0.8, 0.0, 0.17248625, 0.8, 0.45449254, 0.0, 0.0, 0.8, 0.38327882, 0.5690753, 0.0, 0.0, 0.8, 0.8, 0.0]"
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-683,56.190000000000005,0.5619000000000001,20289600,50.03551136363637,50.689214631522326,adc_episode_683,"[0.5, 0, 0.3125, 0.75, 0.75, 0, 0.75, 0.375, 0, 0, 0.25, 0.09375, 0, 0, 0, 0.546875, 0.796875, 0, 0.703125]","[0.5466863, 0.0, 0.35188147, 0.8, 0.8, 0.0, 0.8, 0.38128847, 0.0, 0.0, 0.27877095, 0.11255471, 0.0, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0]"
-684,66.88000000000001,0.6688000000000001,20279808,50.01136363636364,60.5264927380312,adc_episode_684,"[0.75, 0.25, 0.5625, 0, 0.75, 0.4375, 0, 0.34375, 0.78125, 0.09375, 0.21875, 0, 0, 0, 0, 0, 0.40625, 0.671875, 0.40625]","[0.8, 0.30801448, 0.6193127, 0.0, 0.8, 0.44176438, 0.0, 0.36527756, 0.8, 0.104213856, 0.23353313, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-690,44.699999999999996,0.44699999999999995,19948032,49.19318181818182,71.06643356643356,adc_episode_690,"[0.75, 0.125, 0.75, 0.625, 0.0625, 0.75, 0, 0, 0.75, 0.75, 0, 0, 0.4375, 0.625, 0.046875, 0, 0, 0, 0]","[0.8, 0.17228033, 0.8, 0.67717934, 0.09227389, 0.8, 0.0, 0.0, 0.77551496, 0.77612096, 0.0, 0.0, 0.4430749, 0.62530524, 0.052741855, 0.0, 0.0, 0.0, 0.0]"
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-693,55.309999999999995,0.5530999999999999,20276352,50.00284090909091,66.27555137170522,adc_episode_693,"[0.25, 0.75, 0, 0.375, 0.75, 0.75, 0, 0, 0.46875, 0.03125, 0, 0.78125, 0, 0, 0, 0, 0, 0.71875, 0.328125]","[0.25061005, 0.8, 0.0, 0.43306392, 0.8, 0.8, 0.0, 0.0, 0.47241113, 0.058871973, 0.0, 0.8, 0.025692921, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-694,67.24,0.6724,20286720,50.028409090909086,52.578671328671334,adc_episode_694,"[0.75, 0.375, 0.1875, 0.625, 0.6875, 0, 0.0625, 0, 0.78125, 0, 0.125, 0.09375, 0.09375, 0, 0, 0.34375, 0.53125, 0.5625, 0.546875]","[0.8, 0.3937267, 0.20206341, 0.6698332, 0.7440609, 0.0, 0.06653683, 0.0, 0.8, 0.0, 0.12906425, 0.119515955, 0.09513848, 0.0041792216, 0.0, 0.3490822, 0.0, 0.5132153, 0.0072551747]"
-695,62.92,0.6292,20279808,50.01136363636364,50.127756858526084,adc_episode_695,"[0.75, 0, 0.375, 0.25, 0.6875, 0.75, 0, 0, 0.375, 0, 0, 0.21875, 0.1875, 0, 0, 0.53125, 0.5625, 0.546875, 0.5625]","[0.8, 0.0, 0.42785928, 0.3046059, 0.72592074, 0.8, 0.0, 0.0, 0.3939916, 0.0, 0.0, 0.2346617, 0.19700056, 0.0, 0.0, 0.0, 0.4727809, 0.52009964, 0.0]"
-696,71.74000000000001,0.7174,20299392,50.05965909090909,68.4575040344271,adc_episode_696,"[0.625, 0.75, 0.75, 0.75, 0, 0.75, 0, 0.78125, 0.4375, 0, 0.125, 0, 0, 0.3125, 0, 0, 0, 0.21875, 0.734375]","[0.6308673, 0.8, 0.8, 0.8, 0.0, 0.8, 0.0, 0.8, 0.46591604, 0.0, 0.13181584, 0.0, 0.0, 0.32109895, 0.0, 0.0, 0.0, 0.0, 0.0]"
-697,74.57000000000001,0.7457,20291904,50.04119318181818,67.61699838622916,BEST_adc_episode_697,"[0.5625, 0.75, 0.1875, 0.75, 0.5, 0.125, 0, 0.375, 0.5, 0.28125, 0.5625, 0.25, 0, 0, 0, 0, 0, 0.515625, 0.578125]","[0.6236598, 0.8, 0.20041694, 0.8, 0.55473393, 0.15515134, 0.0, 0.38144332, 0.5091079, 0.29751626, 0.5825543, 0.2630597, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5136711]"
-698,52.17,0.5217,20280960,50.01420454545455,64.16420118343196,adc_episode_698,"[0, 0.75, 0.75, 0, 0, 0.375, 0, 0, 0.78125, 0.40625, 0.6875, 0.5625, 0.25, 0, 0, 0, 0, 0.46875, 0.640625]","[0.0, 0.8, 0.8, 0.0, 0.0, 0.41442636, 0.025571564, 0.0, 0.8, 0.4277497, 0.69616944, 0.5843442, 0.26667687, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12526572]"
-699,61.24000000000001,0.6124,20297088,50.053977272727266,70.21920387305003,adc_episode_699,"[0.0625, 0.5, 0.5, 0, 0.75, 0.0625, 0.625, 0.78125, 0.71875, 0, 0.75, 0, 0, 0, 0, 0, 0, 0.21875, 0.734375]","[0.096598156, 0.5198184, 0.5585115, 0.0, 0.8, 0.0690665, 0.6701817, 0.8, 0.74200696, 0.0, 0.7577337, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-701,55.57000000000001,0.5557000000000001,20295936,50.05113636363636,73.55096826250673,adc_episode_701,"[0.75, 0.75, 0, 0.75, 0.75, 0, 0, 0.78125, 0.53125, 0.25, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.765625]","[0.8, 0.8, 0.0, 0.8, 0.8, 0.0, 0.0, 0.8, 0.54445684, 0.27922937, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-702,38.01,0.3801,20309184,50.08380681818182,62.6647391070468,adc_episode_702,"[0.75, 0.75, 0, 0.75, 0, 0, 0, 0.6875, 0, 0.625, 0.4375, 0, 0.78125, 0.796875, 0, 0, 0, 0, 0.3125]","[0.8, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.6988501, 0.0, 0.64337647, 0.45011035, 0.0, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-715,37.61,0.3761,20275200,50.0,62.38569123184507,adc_episode_715,"[0.75, 0.75, 0.75, 0, 0.75, 0, 0.1875, 0, 0, 0.78125, 0, 0, 0.59375, 0, 0.703125, 0, 0, 0, 0.1875]","[0.8, 0.8, 0.8, 0.0, 0.8, 0.0, 0.22060968, 0.0, 0.0, 0.8, 0.0, 0.0, 0.59730375, 0.0, 0.7143741, 0.0, 0.0, 0.0, 0.0]"
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-1014,68.41,0.6840999999999999,20279232,50.00994318181819,69.67119419042496,adc_episode_1014,"[0.75, 0.5, 0.75, 0, 0.75, 0.75, 0.75, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0.03125, 0, 0.515625, 0.609375]","[0.8, 0.5044124, 0.8, 0.0, 0.8, 0.7641617, 0.75747997, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.040976666, 0.0, 0.0, 0.0]"
-1015,69.89000000000001,0.6989000000000002,20282688,50.018465909090914,67.65398063474987,adc_episode_1015,"[0.75, 0.75, 0, 0.75, 0.5, 0, 0.75, 0.03125, 0.78125, 0.40625, 0, 0, 0, 0, 0, 0, 0, 0.640625, 0.453125]","[0.8, 0.8, 0.0, 0.8, 0.54170424, 0.0, 0.8, 0.057788014, 0.8, 0.412057, 0.030560885, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1016,70.82,0.7081999999999999,20279808,50.01136363636364,62.45293168370092,adc_episode_1016,"[0.75, 0.75, 0.75, 0.5, 0.5, 0.75, 0.0625, 0, 0, 0.375, 0, 0.21875, 0, 0, 0, 0, 0.484375, 0.625, 0.46875]","[0.8, 0.8, 0.8, 0.5004758, 0.5571613, 0.8, 0.097084716, 0.0, 0.0, 0.39163783, 0.0033675889, 0.22036347, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1017,76.25,0.7625,20289024,50.034090909090914,68.49784830554061,BEST_adc_episode_1017,"[0.5625, 0.75, 0.75, 0.5625, 0.4375, 0.375, 0.125, 0, 0.25, 0.65625, 0.625, 0, 0, 0, 0, 0, 0, 0.5625, 0.53125]","[0.5737997, 0.8, 0.8, 0.56793356, 0.49639654, 0.40173155, 0.14957164, 0.0, 0.25878945, 0.6700395, 0.6477942, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-1021,66.62,0.6662,20275200,50.0,61.296395911780536,adc_episode_1021,"[0.75, 0.75, 0.5, 0.5625, 0.75, 0.375, 0, 0.21875, 0.5, 0.125, 0, 0, 0.25, 0, 0, 0, 0.359375, 0.6875, 0.390625]","[0.8, 0.8, 0.52267116, 0.5989628, 0.8, 0.42323658, 0.0, 0.23781666, 0.5048243, 0.12786867, 0.0, 0.0, 0.27599925, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-1024,64.94,0.6494,20297088,50.053977272727266,70.6024744486283,adc_episode_1024,"[0.75, 0.75, 0.75, 0.75, 0, 0.75, 0.375, 0, 0, 0.53125, 0, 0.34375, 0.0625, 0, 0.109375, 0, 0, 0.265625, 0.71875]","[0.8, 0.8, 0.8, 0.8, 0.0, 0.8, 0.41581926, 0.0, 0.0, 0.5354175, 0.0, 0.36948198, 0.086408496, 0.0, 0.11942646, 0.0, 0.0, 0.0, 0.0]"
-1025,69.03,0.6903,20283264,50.01988636363637,69.74515868746639,adc_episode_1025,"[0.625, 0.75, 0.6875, 0.5625, 0.75, 0.125, 0.1875, 0, 0.78125, 0, 0.40625, 0, 0.09375, 0, 0, 0, 0, 0.40625, 0.671875]","[0.67819375, 0.8, 0.6900446, 0.6036879, 0.8, 0.14128242, 0.24174792, 0.0, 0.8, 0.0, 0.42185536, 0.0, 0.11351815, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-1027,73.94000000000001,0.7394000000000002,20291328,50.03977272727273,69.84265734265735,adc_episode_1027,"[0.75, 0.75, 0, 0.5625, 0.375, 0.75, 0, 0, 0.78125, 0.1875, 0.46875, 0, 0, 0, 0, 0, 0, 0.4375, 0.640625]","[0.8, 0.8, 0.0, 0.59579754, 0.406029, 0.8, 0.0, 0.0, 0.8, 0.19376703, 0.48402435, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1028,63.42,0.6342,20278656,50.008522727272734,66.58149542764927,adc_episode_1028,"[0.625, 0, 0.75, 0.75, 0.75, 0.75, 0.1875, 0, 0.71875, 0, 0.15625, 0, 0, 0, 0, 0, 0.046875, 0.78125, 0.125]","[0.6599908, 0.0, 0.8, 0.8, 0.8, 0.8, 0.2038018, 0.0, 0.7304134, 0.0, 0.16087635, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1029,72.3,0.723,20307456,50.07954545454546,75.27232383001615,adc_episode_1029,"[0, 0.75, 0.6875, 0.5625, 0.75, 0, 0.75, 0, 0.34375, 0, 0.6875, 0.34375, 0, 0, 0, 0, 0, 0, 0.765625]","[0.0, 0.8, 0.68935555, 0.6090856, 0.8, 0.0, 0.8, 0.0, 0.35086593, 0.0, 0.6899912, 0.35605198, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1030,71.85000000000001,0.7185000000000001,20289024,50.034090909090914,65.06858526089296,adc_episode_1030,"[0.75, 0.75, 0.0625, 0.75, 0, 0.5625, 0, 0.21875, 0.3125, 0, 0.4375, 0.375, 0.1875, 0, 0, 0, 0.453125, 0, 0.640625]","[0.8, 0.8, 0.063707836, 0.8, 0.0, 0.5730567, 0.0, 0.24464497, 0.33511016, 0.0, 0.45323017, 0.39245778, 0.20282474, 0.0, 0.0, 0.0, 0.4596558, 0.0, 0.0]"
-1031,61.5,0.615,20284416,50.02272727272727,42.69096288327058,adc_episode_1031,"[0.75, 0.5625, 0.75, 0.625, 0.375, 0.1875, 0, 0, 0.09375, 0, 0.1875, 0, 0, 0.421875, 0, 0.71875, 0.296875, 0.71875, 0.28125]","[0.8, 0.6067636, 0.8, 0.6682162, 0.39739662, 0.19265622, 0.0, 0.0, 0.11575216, 0.0, 0.20350926, 0.0, 0.0, 0.43520752, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1032,69.57,0.6957,20318976,50.10795454545455,68.62224314147392,adc_episode_1032,"[0.75, 0.6875, 0.75, 0.75, 0.6875, 0, 0, 0.46875, 0.78125, 0.375, 0, 0.1875, 0, 0.34375, 0, 0, 0, 0, 0.796875]","[0.8, 0.73427385, 0.8, 0.8, 0.73166794, 0.0, 0.0008950027, 0.49250406, 0.8, 0.3945374, 0.0, 0.19901276, 0.0, 0.35279652, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1033,57.74,0.5774,20301696,50.06534090909091,53.412452931683696,adc_episode_1033,"[0.0625, 0.75, 0.375, 0.75, 0.5625, 0.75, 0, 0, 0.25, 0, 0, 0.09375, 0, 0, 0.15625, 0.21875, 0.75, 0.21875, 0.734375]","[0.07142683, 0.7817281, 0.43075213, 0.8, 0.59154755, 0.8, 0.0, 0.0, 0.28113177, 0.0, 0.0, 0.11416582, 0.0, 0.0, 0.16779245, 0.0, 0.0, 0.0, 0.0]"
-1034,70.76,0.7076,20296512,50.05255681818181,61.79397525551372,adc_episode_1034,"[0.75, 0.25, 0.75, 0.6875, 0.0625, 0.3125, 0.5, 0, 0.15625, 0, 0.78125, 0.09375, 0, 0, 0, 0.5, 0, 0.140625, 0.765625]","[0.8, 0.25824872, 0.8, 0.72741, 0.08917444, 0.3187844, 0.56072515, 0.0, 0.183853, 0.0, 0.8, 0.11399746, 0.0, 0.0, 0.0, 0.5092913, 0.0, 0.0, 0.0]"
-1035,67.6,0.6759999999999999,20284992,50.02414772727273,62.80594405594405,adc_episode_1035,"[0.5, 0.75, 0.625, 0.75, 0, 0.75, 0, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.390625, 0.671875, 0.40625]","[0.5498645, 0.8, 0.63904774, 0.8, 0.053008817, 0.8, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1036,69.67,0.6967,20289024,50.034090909090914,74.53940290478752,adc_episode_1036,"[0.3125, 0.375, 0.75, 0.6875, 0.125, 0.75, 0, 0.0625, 0.78125, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.765625]","[0.33529082, 0.4313824, 0.8, 0.73589367, 0.17622983, 0.7684557, 0.0, 0.08480565, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1037,61.01,0.6101,20278656,50.008522727272734,48.19459386767079,adc_episode_1037,"[0.75, 0.75, 0.75, 0.4375, 0, 0, 0.125, 0.40625, 0.46875, 0, 0.375, 0, 0, 0, 0, 0.609375, 0.484375, 0.609375, 0.5]","[0.79938763, 0.8, 0.8, 0.45851505, 0.0, 0.061305765, 0.17253298, 0.41328087, 0.469833, 0.0, 0.386465, 0.010425837, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1038,63.23,0.6323,20291904,50.04119318181818,52.78039268423884,adc_episode_1038,"[0.75, 0.6875, 0.5625, 0.5625, 0.75, 0.0625, 0.125, 0, 0.03125, 0.34375, 0.21875, 0, 0, 0, 0, 0.453125, 0.640625, 0.4375, 0.640625]","[0.8, 0.7307355, 0.57036465, 0.5660811, 0.750666, 0.069950074, 0.14668655, 0.0, 0.036110636, 0.36958686, 0.2423067, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2409919]"
-1039,68.91000000000001,0.6891000000000002,20275200,50.0,65.20979020979021,adc_episode_1039,"[0.75, 0, 0.75, 0.6875, 0.75, 0, 0.3125, 0, 0.34375, 0, 0.78125, 0, 0, 0, 0.140625, 0, 0.46875, 0, 0.421875]","[0.8, 0.0, 0.8, 0.7064556, 0.8, 0.0, 0.3277565, 0.0, 0.37116522, 0.0, 0.8, 0.0, 0.0, 0.0, 0.1478291, 0.0, 0.47911865, 0.0, 0.0]"
-1040,68.77,0.6877,20280960,50.01420454545455,58.64712210866057,adc_episode_1040,"[0.3125, 0.625, 0.3125, 0.75, 0.1875, 0.75, 0, 0, 0.34375, 0.46875, 0, 0, 0.09375, 0, 0, 0, 0.671875, 0.390625, 0.671875]","[0.36178783, 0.63364965, 0.3184736, 0.8, 0.21078734, 0.8, 0.0, 0.0, 0.36066508, 0.47645107, 0.0, 0.0, 0.11860101, 0.0, 0.0, 0.0, 0.0, 0.0, 0.31886137]"
-1041,63.629999999999995,0.6363,20281536,50.015625,67.74811726734804,adc_episode_1041,"[0.75, 0.75, 0.25, 0.4375, 0.75, 0.75, 0, 0, 0.78125, 0, 0, 0.25, 0, 0, 0, 0, 0, 0.703125, 0.359375]","[0.8, 0.7990141, 0.30957076, 0.44547755, 0.8, 0.8, 0.0, 0.0, 0.8, 0.0, 0.0, 0.26210618, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1042,75.14,0.7514,20140416,49.66761363636364,61.68975255513717,adc_episode_1042,"[0.75, 0.75, 0.75, 0.5625, 0.5, 0.25, 0.1875, 0.28125, 0.375, 0, 0.40625, 0, 0, 0, 0.21875, 0, 0, 0.703125, 0.53125]","[0.8, 0.8, 0.8, 0.5767539, 0.5375256, 0.29676196, 0.1940989, 0.28603014, 0.40548643, 0.0, 0.43591982, 0.0, 0.0, 0.0, 0.22800387, 0.0, 0.0, 0.0, 0.53709435]"
-1043,57.34,0.5734,20276352,50.00284090909091,58.21678321678322,adc_episode_1043,"[0.6875, 0.75, 0.75, 0, 0.75, 0, 0.25, 0, 0.09375, 0.78125, 0, 0, 0, 0, 0.734375, 0, 0, 0.03125, 0.796875]","[0.71875936, 0.8, 0.8, 0.0, 0.8, 0.0, 0.30126727, 0.0, 0.09953519, 0.8, 0.0, 0.0, 0.0, 0.0, 0.7406611, 0.0, 0.0, 0.0, 0.0]"
-1044,45.25,0.4525,20296512,50.05255681818181,57.14429800968263,adc_episode_1044,"[0, 0.75, 0.75, 0.75, 0.75, 0.3125, 0, 0, 0.25, 0.21875, 0.03125, 0.21875, 0.0625, 0, 0, 0.03125, 0.796875, 0.046875, 0.78125]","[0.0, 0.8, 0.75392467, 0.8, 0.8, 0.31293604, 0.0, 0.0, 0.26580846, 0.24493968, 0.058323953, 0.2217808, 0.071965925, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1045,67.96,0.6796,20282112,50.01704545454545,63.908687466379774,adc_episode_1045,"[0.5, 0.75, 0.5625, 0.625, 0.625, 0.25, 0.4375, 0, 0.78125, 0.09375, 0, 0, 0, 0, 0, 0, 0.234375, 0.75, 0.203125]","[0.5082202, 0.8, 0.6022386, 0.66671866, 0.6501144, 0.2791072, 0.48335648, 0.01265014, 0.8, 0.10856598, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-1123,71.50000000000001,0.7150000000000002,20278656,50.008522727272734,65.64349112426035,adc_episode_1123,"[0.75, 0.6875, 0.75, 0.75, 0.375, 0.4375, 0.5625, 0.0625, 0, 0.65625, 0, 0, 0, 0, 0, 0, 0.140625, 0.78125, 0.09375]","[0.8, 0.72342855, 0.8, 0.8, 0.3874571, 0.47551396, 0.6199186, 0.08131383, 0.0, 0.66836625, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1124,65.63,0.6563,20306304,50.07670454545454,72.97606239913932,adc_episode_1124,"[0.75, 0.75, 0.0625, 0.5, 0.75, 0.25, 0.25, 0.78125, 0, 0.21875, 0, 0.78125, 0, 0, 0, 0, 0, 0.09375, 0.765625]","[0.8, 0.8, 0.0795132, 0.5572027, 0.8, 0.2884, 0.26186875, 0.8, 0.006874091, 0.24499945, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1125,72.96000000000001,0.7296,20276352,50.00284090909091,65.34763313609467,adc_episode_1125,"[0.6875, 0.0625, 0.75, 0.75, 0.4375, 0.3125, 0.75, 0, 0.4375, 0.1875, 0.28125, 0, 0, 0, 0, 0, 0.09375, 0.78125, 0.109375]","[0.7092467, 0.106801145, 0.8, 0.8, 0.45563522, 0.32318416, 0.7576266, 0.0, 0.45435375, 0.2118274, 0.28398737, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1126,63.85000000000001,0.6385000000000001,20276928,50.00426136363636,66.99502420656268,adc_episode_1126,"[0, 0.75, 0.75, 0.75, 0.75, 0, 0.75, 0, 0.4375, 0.4375, 0, 0, 0, 0, 0, 0, 0, 0.796875, 0.015625]","[0.061274193, 0.8, 0.7860932, 0.7824934, 0.8, 0.0, 0.8, 0.0, 0.4630979, 0.467232, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1127,58.56,0.5856,20293632,50.04545454545455,71.5505648197956,adc_episode_1127,"[0.75, 0.75, 0.75, 0.3125, 0.75, 0.75, 0.25, 0.15625, 0.53125, 0.40625, 0, 0, 0, 0, 0, 0, 0, 0.453125, 0.625]","[0.8, 0.8, 0.8, 0.3459227, 0.8, 0.8, 0.2544798, 0.18079801, 0.53846395, 0.4343457, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1128,67.78,0.6778,20311488,50.08948863636363,67.94311457772996,adc_episode_1128,"[0.125, 0.75, 0.75, 0.75, 0.75, 0, 0.75, 0, 0, 0.78125, 0, 0, 0, 0.0625, 0, 0.296875, 0.078125, 0, 0.703125]","[0.17454495, 0.8, 0.8, 0.8, 0.8, 0.0, 0.8, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.07326956, 0.0, 0.30649835, 0.09189899, 0.0, 0.0]"
-1129,65.93,0.6593000000000001,20283264,50.01988636363637,61.31320602474448,adc_episode_1129,"[0, 0.75, 0.625, 0, 0.75, 0, 0.75, 0.78125, 0, 0, 0, 0.34375, 0.15625, 0, 0, 0, 0.328125, 0.71875, 0.28125]","[0.0, 0.8, 0.67365545, 0.0, 0.8, 0.0, 0.8, 0.8, 0.0, 0.0, 0.0, 0.346628, 0.18058446, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1130,73.29,0.7329000000000001,20068992,49.49147727272727,59.20521785906401,adc_episode_1130,"[0.75, 0.5625, 0.75, 0.75, 0.4375, 0.75, 0.1875, 0, 0.3125, 0, 0.09375, 0, 0, 0, 0.078125, 0, 0.546875, 0.578125, 0.734375]","[0.77192, 0.616109, 0.8, 0.7709778, 0.45820305, 0.8, 0.21234812, 0.0, 0.32445982, 0.0, 0.10939079, 0.0, 0.0, 0.0, 0.087751895, 0.0, 0.0, 0.0, 0.73509747]"
-1131,71.75,0.7175,20282112,50.01704545454545,65.85529854760624,adc_episode_1131,"[0.5, 0.5, 0.75, 0.625, 0.75, 0.75, 0, 0, 0.53125, 0, 0, 0.28125, 0, 0, 0, 0.171875, 0, 0.59375, 0.53125]","[0.5370843, 0.52216303, 0.8, 0.6812327, 0.8, 0.8, 0.0, 0.0, 0.5525693, 0.0, 0.0, 0.30484435, 0.0, 0.0, 0.0, 0.18085307, 0.0, 0.0, 0.0]"
-1132,70.44,0.7044,20298816,50.05823863636364,74.80836471221086,adc_episode_1132,"[0, 0.6875, 0.75, 0.75, 0, 0.75, 0.125, 0.46875, 0.0625, 0.78125, 0, 0.3125, 0, 0, 0, 0, 0, 0.015625, 0.765625]","[0.0, 0.7346789, 0.8, 0.8, 0.0, 0.8, 0.12522809, 0.47422254, 0.07644743, 0.8, 0.0, 0.34285858, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019037995, 0.0]"
-1133,66.21000000000001,0.6621000000000001,20297088,50.053977272727266,66.76976869284562,adc_episode_1133,"[0.375, 0.75, 0.75, 0.1875, 0.75, 0.75, 0, 0, 0.78125, 0, 0, 0.125, 0, 0, 0, 0, 0.390625, 0.109375, 0.765625]","[0.3819497, 0.8, 0.8, 0.23210262, 0.8, 0.8, 0.0, 0.01497091, 0.8, 0.0, 0.0, 0.1305761, 0.0, 0.0, 0.0, 0.0, 0.4049585, 0.0, 0.0]"
-1134,76.85,0.7685,20308608,50.08238636363637,74.11915008068854,BEST_adc_episode_1134,"[0.75, 0.625, 0.75, 0.5625, 0.75, 0, 0.4375, 0, 0.5, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0.078125, 0.78125]","[0.8, 0.66174793, 0.8, 0.62411183, 0.8, 0.0, 0.4656206, 0.0, 0.52246624, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1135,60.58,0.6058,20283840,50.02130681818182,69.6980903711673,adc_episode_1135,"[0.75, 0.75, 0.75, 0.125, 0.75, 0.375, 0.75, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0.578125, 0.546875]","[0.8, 0.8, 0.7855945, 0.18055871, 0.8, 0.41827115, 0.78693104, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1136,68.03,0.6803,20275776,50.00142045454545,54.96570736955352,adc_episode_1136,"[0.75, 0.75, 0.4375, 0.75, 0.3125, 0, 0.5, 0, 0.03125, 0.375, 0, 0.25, 0, 0.171875, 0, 0.421875, 0, 0.703125, 0.328125]","[0.8, 0.8, 0.4904011, 0.8, 0.32841998, 0.0, 0.5595326, 0.0, 0.041430086, 0.39587325, 0.0, 0.26042843, 0.0, 0.18681405, 0.0, 0.43106598, 0.0, 0.0, 0.0]"
-1137,56.96,0.5696,20282112,50.01704545454545,66.86390532544378,adc_episode_1137,"[0.75, 0.75, 0.3125, 0.75, 0.125, 0.75, 0, 0, 0.75, 0, 0, 0.21875, 0, 0, 0, 0, 0, 0.796875, 0.0625]","[0.8, 0.8, 0.348574, 0.8, 0.14029914, 0.8, 0.0, 0.0, 0.76946855, 0.0, 0.0, 0.23311579, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1138,61.79,0.6179,20297088,50.053977272727266,70.7235072619688,adc_episode_1138,"[0.5, 0, 0.625, 0, 0.1875, 0.75, 0.75, 0.1875, 0.1875, 0.78125, 0, 0.78125, 0, 0, 0, 0, 0, 0.109375, 0.78125]","[0.54970753, 0.0, 0.6455636, 0.045106124, 0.23412757, 0.75407887, 0.8, 0.19550936, 0.2147905, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1139,70.32000000000001,0.7032,20278080,50.00710227272728,60.28442711135019,adc_episode_1139,"[0.75, 0.25, 0.75, 0.75, 0.5625, 0, 0.5625, 0.3125, 0.4375, 0, 0, 0.15625, 0, 0, 0.125, 0, 0.296875, 0.734375, 0.25]","[0.8, 0.2778879, 0.8, 0.8, 0.6198843, 0.0, 0.618233, 0.3142947, 0.44489396, 0.0, 0.0, 0.16192436, 0.0, 0.0, 0.13729557, 0.0, 0.0, 0.0, 0.0]"
-1140,61.39,0.6139,20286720,50.028409090909086,80.61793975255513,adc_episode_1140,"[0.75, 0.625, 0.6875, 0.75, 0.625, 0.5, 0, 0, 0.78125, 0.0625, 0.03125, 0.78125, 0, 0, 0, 0, 0, 0, 0.28125]","[0.8, 0.6567622, 0.7173815, 0.8, 0.6368652, 0.54301995, 0.0, 0.0, 0.8, 0.08801528, 0.04239906, 0.7980761, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1141,57.42,0.5742,20279808,50.01136363636364,65.97296933835395,adc_episode_1141,"[0.75, 0.75, 0.75, 0, 0.625, 0.4375, 0.6875, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.078125, 0.78125, 0.109375]","[0.8, 0.8, 0.8, 0.0, 0.6620186, 0.4883251, 0.72070557, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.046577014]"
-1142,69.61,0.6960999999999999,20297664,50.055397727272734,69.93006993006993,adc_episode_1142,"[0.6875, 0.5, 0.375, 0, 0.4375, 0.75, 0, 0.78125, 0.78125, 0.40625, 0, 0.40625, 0, 0, 0, 0, 0, 0.265625, 0.734375]","[0.7129779, 0.55637026, 0.38140753, 0.0, 0.48538104, 0.8, 0.0, 0.8, 0.8, 0.41431588, 0.0, 0.43074664, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1143,58.35,0.5835,20275200,50.0,79.33028509951588,adc_episode_1143,"[0.75, 0.75, 0.75, 0.625, 0.75, 0.75, 0, 0, 0.78125, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0.515625]","[0.8, 0.8, 0.8, 0.6346481, 0.8, 0.8, 0.0, 0.0, 0.8, 0.0, 0.50176305, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1144,45.39,0.4539,20282112,50.01704545454545,64.52393760086068,adc_episode_1144,"[0.75, 0.75, 0.75, 0, 0.75, 0, 0, 0, 0.78125, 0, 0, 0.5625, 0, 0, 0, 0, 0.03125, 0.796875, 0.03125]","[0.8, 0.8, 0.8, 0.0, 0.7977993, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.5760918, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1145,67.16,0.6716,20282112,50.01704545454545,65.86538461538461,adc_episode_1145,"[0.4375, 0.5, 0.5625, 0.75, 0.5625, 0.75, 0.4375, 0, 0, 0.6875, 0.125, 0, 0, 0, 0, 0, 0.109375, 0.78125, 0.109375]","[0.4498431, 0.5392992, 0.5712896, 0.8, 0.56453794, 0.8, 0.4837552, 0.0, 0.0, 0.7150564, 0.13778098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1146,44.14,0.4414,20087424,49.53693181818182,82.00645508337816,adc_episode_1146,"[0.75, 0.75, 0.625, 0.75, 0.75, 0, 0.75, 0.4375, 0.46875, 0, 0, 0.78125, 0, 0, 0, 0, 0, 0.046875, 0.09375]","[0.8, 0.8, 0.64699847, 0.8, 0.8, 0.0, 0.8, 0.44500622, 0.48547316, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04882756, 0.101402186]"
-1147,66.01,0.6601,20289024,50.034090909090914,74.32759548144163,adc_episode_1147,"[0.375, 0.75, 0.75, 0.125, 0.4375, 0.75, 0.75, 0.46875, 0.4375, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.0625, 0.78125]","[0.4168599, 0.8, 0.8, 0.15342623, 0.48131615, 0.8, 0.8, 0.48029813, 0.44987115, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1148,72.98,0.7298,20289024,50.034090909090914,71.36229155459924,adc_episode_1148,"[0.75, 0.5, 0.75, 0, 0.75, 0.75, 0, 0.34375, 0.3125, 0.25, 0.5, 0, 0, 0, 0, 0, 0, 0.375, 0.6875]","[0.8, 0.5042609, 0.8, 0.009659771, 0.8, 0.8, 0.0, 0.362002, 0.33502194, 0.27125245, 0.50077325, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1149,69.03,0.6903,20312064,50.090909090909086,74.02837547068316,adc_episode_1149,"[0.75, 0.1875, 0.6875, 0.75, 0.625, 0.3125, 0, 0.125, 0.78125, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.796875]","[0.8, 0.21919623, 0.7103361, 0.8, 0.65039766, 0.33767143, 0.0, 0.12557831, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1150,55.230000000000004,0.5523,20278080,50.00710227272728,66.35623991393223,adc_episode_1150,"[0.75, 0.75, 0.75, 0.6875, 0.75, 0.125, 0.75, 0, 0.5, 0.03125, 0, 0, 0, 0, 0, 0, 0.140625, 0.765625, 0.1875]","[0.8, 0.8, 0.8, 0.73621255, 0.8, 0.14127967, 0.8, 0.0, 0.50584996, 0.04474255, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1151,60.69,0.6069,20287296,50.02982954545454,67.6405325443787,adc_episode_1151,"[0.6875, 0.75, 0.75, 0.0625, 0.4375, 0, 0.75, 0.40625, 0.5, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.578125, 0.515625]","[0.7376226, 0.8, 0.8, 0.11560767, 0.4688161, 0.0, 0.8, 0.42967698, 0.52340233, 0.8, 0.017475411, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1152,56.76,0.5676,20276928,50.00426136363636,65.72417966648736,adc_episode_1152,"[0.375, 0.75, 0.75, 0, 0.75, 0.6875, 0, 0.25, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0.09375, 0.765625, 0.203125]","[0.42784086, 0.8, 0.8, 0.0, 0.8, 0.6931172, 0.0, 0.2696823, 0.029267207, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1153,46.95,0.46950000000000003,20282112,50.01704545454545,72.17926304464766,adc_episode_1153,"[0.625, 0.75, 0.0625, 0.75, 0.75, 0.75, 0, 0, 0.78125, 0, 0.09375, 0.28125, 0, 0, 0, 0.53125, 0, 0, 0.0625]","[0.6675459, 0.8, 0.064598724, 0.8, 0.8, 0.8, 0.0, 0.0, 0.8, 0.0, 0.11422367, 0.3111822, 0.0, 0.0, 0.0, 0.536496, 0.0, 0.0, 0.0]"
-1154,78.08,0.7807999999999999,20284416,50.02272727272727,60.765196342119424,BEST_adc_episode_1154,"[0.75, 0.25, 0.375, 0.75, 0.4375, 0.25, 0.125, 0.15625, 0.5, 0, 0.34375, 0.375, 0, 0, 0.25, 0, 0, 0.65625, 0.4375]","[0.8, 0.29221278, 0.4152894, 0.8, 0.4559997, 0.2678666, 0.13290296, 0.15757975, 0.5199381, 0.0, 0.35579953, 0.39995405, 0.0, 0.0, 0.25451437, 0.0, 0.0, 0.0, 0.0]"
-1155,60.339999999999996,0.6033999999999999,20282112,50.01704545454545,52.793840774610004,adc_episode_1155,"[0.0625, 0.625, 0.3125, 0.4375, 0.3125, 0.75, 0, 0, 0.75, 0, 0.0625, 0, 0, 0, 0, 0.359375, 0.6875, 0.359375, 0.6875]","[0.101253085, 0.6446626, 0.3455198, 0.45994714, 0.37138703, 0.8, 0.0, 0.0, 0.7510404, 0.0, 0.06709406, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1156,73.13000000000001,0.7313000000000001,20287872,50.03125000000001,67.05890263582572,adc_episode_1156,"[0.625, 0.625, 0.625, 0.75, 0, 0.75, 0.4375, 0, 0.3125, 0.46875, 0, 0.125, 0, 0, 0, 0.34375, 0, 0.109375, 0.78125]","[0.68519825, 0.6316781, 0.68474925, 0.8, 0.0, 0.8, 0.4736217, 0.0, 0.3154361, 0.4828054, 0.0, 0.13697664, 0.0, 0.0, 0.0, 0.3511501, 0.0, 0.0, 0.0]"
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-1949,69.28,0.6928,20297664,50.055397727272734,54.65976331360947,adc_episode_1949,"[0.75, 0.75, 0.3125, 0.75, 0.4375, 0.1875, 0, 0.34375, 0.09375, 0.21875, 0, 0.28125, 0, 0, 0, 0.21875, 0.734375, 0.265625, 0.71875]","[0.8, 0.8, 0.34186482, 0.8, 0.45098498, 0.24337254, 0.0, 0.35878882, 0.11345701, 0.23076452, 0.0, 0.28662613, 0.0, 0.0, 0.0, 0.0, 0.43140385, 0.0, 0.0]"
-1950,76.68,0.7668,20293632,50.04545454545455,74.56629908552985,adc_episode_1950,"[0.75, 0.5, 0.6875, 0.75, 0.5, 0.5625, 0.0625, 0.78125, 0, 0.65625, 0.15625, 0, 0, 0, 0, 0, 0, 0.0625, 0.78125]","[0.8, 0.56017154, 0.7352736, 0.8, 0.5102353, 0.60239476, 0.11655247, 0.78275204, 0.0, 0.68093866, 0.18308511, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1951,74.91,0.7491,20286144,50.02698863636363,63.82127487896718,adc_episode_1951,"[0.75, 0.75, 0.625, 0.625, 0.75, 0, 0.1875, 0, 0.59375, 0.375, 0, 0, 0.28125, 0.21875, 0, 0, 0, 0.578125, 0.515625]","[0.8, 0.8, 0.6551986, 0.6699141, 0.8, 0.015728291, 0.24156392, 0.0, 0.6164725, 0.40365446, 0.0, 0.0, 0.2897148, 0.22933774, 0.0, 0.0, 0.0, 0.0, 0.39383373]"
-1952,71.91000000000001,0.7191000000000001,20295936,50.05113636363636,78.3317643894567,adc_episode_1952,"[0.75, 0.625, 0.3125, 0.75, 0, 0.75, 0, 0.375, 0.78125, 0.46875, 0.5625, 0, 0, 0, 0, 0, 0, 0, 0.453125]","[0.7809271, 0.6645154, 0.3521478, 0.8, 0.0, 0.8, 0.0, 0.3799339, 0.8, 0.48792264, 0.57612234, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1953,72.78,0.7278,20291328,50.03977272727273,70.19566971490049,adc_episode_1953,"[0.75, 0.75, 0.6875, 0.125, 0.6875, 0.25, 0, 0.65625, 0.65625, 0, 0.1875, 0.40625, 0, 0, 0.203125, 0, 0, 0, 0.734375]","[0.8, 0.8, 0.68986976, 0.15301262, 0.7130909, 0.267295, 0.0, 0.6602086, 0.6602684, 0.0, 0.20798583, 0.42913234, 0.0, 0.0, 0.20912294, 0.0, 0.0, 0.0, 0.0]"
-1954,70.13000000000001,0.7013000000000001,20283264,50.01988636363637,68.40371167294245,adc_episode_1954,"[0.75, 0.75, 0.75, 0.75, 0.75, 0, 0.375, 0, 0.375, 0, 0.59375, 0, 0, 0, 0, 0, 0, 0.640625, 0.46875]","[0.8, 0.8, 0.8, 0.8, 0.7941403, 0.0, 0.3845117, 0.011170709, 0.38044325, 0.0, 0.6028191, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1955,71.33,0.7132999999999999,20275200,50.0,65.36108122646584,adc_episode_1955,"[0.75, 0.1875, 0.75, 0.25, 0.5625, 0.6875, 0, 0.0625, 0.375, 0.6875, 0.15625, 0, 0, 0, 0, 0, 0.078125, 0.78125, 0.109375]","[0.7844759, 0.24295281, 0.8, 0.2848894, 0.60490143, 0.72648484, 0.0, 0.092894815, 0.3863537, 0.7068756, 0.16763465, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1956,72.39,0.7239,20290176,50.03693181818182,70.33015061861215,adc_episode_1956,"[0.625, 0.4375, 0.625, 0.75, 0, 0.75, 0, 0.15625, 0.78125, 0.6875, 0, 0, 0, 0, 0, 0, 0, 0.421875, 0.65625]","[0.6838032, 0.48640436, 0.6322741, 0.8, 0.0, 0.8, 0.0, 0.17006537, 0.8, 0.7121288, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12726691]"
-1957,74.64,0.7464,20283264,50.01988636363637,60.476062399139316,adc_episode_1957,"[0.75, 0.3125, 0.75, 0, 0.75, 0, 0.5, 0.03125, 0.3125, 0.375, 0.25, 0, 0, 0, 0, 0, 0.5, 0.59375, 0.515625]","[0.8, 0.36767378, 0.75968504, 0.0, 0.8, 0.0, 0.5456704, 0.060211714, 0.32695445, 0.38512754, 0.25117147, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.30523708]"
-1958,60.709999999999994,0.6071,20278080,50.00710227272728,63.612829478214096,adc_episode_1958,"[0.75, 0.75, 0.75, 0.5, 0.6875, 0, 0, 0, 0.78125, 0.40625, 0.21875, 0, 0, 0, 0, 0, 0.15625, 0.765625, 0.171875]","[0.7839809, 0.8, 0.8, 0.54029363, 0.747655, 0.0, 0.0, 0.0, 0.8, 0.41729876, 0.23980162, 0.030325782, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.020984095]"
-1959,67.60999999999999,0.6760999999999998,20278080,50.00710227272728,57.934373318988705,adc_episode_1959,"[0.75, 0.25, 0.75, 0.75, 0, 0.6875, 0, 0.03125, 0.15625, 0.4375, 0, 0.09375, 0, 0, 0, 0.015625, 0.796875, 0.015625, 0.796875]","[0.8, 0.27054387, 0.8, 0.8, 0.0, 0.7314895, 0.0, 0.04881892, 0.17997307, 0.4390422, 0.0, 0.09451684, 0.0, 0.0, 0.0, 0.0, 0.6698021, 0.0, 0.0]"
-1960,64.44,0.6444,20278080,50.00710227272728,64.13730500268962,adc_episode_1960,"[0, 0.75, 0.25, 0.75, 0.75, 0, 0.625, 0, 0.78125, 0, 0, 0.125, 0, 0, 0, 0, 0.171875, 0.765625, 0.15625]","[0.0, 0.8, 0.30526558, 0.8, 0.8, 0.0, 0.64593464, 0.0, 0.8, 0.005146491, 0.0, 0.13395117, 0.0, 0.0, 0.0, 0.0, 0.0, 0.11167649, 0.0]"
-1961,65.74,0.6574,20275200,50.0,71.5034965034965,adc_episode_1961,"[0.75, 0.375, 0.6875, 0.625, 0.75, 0, 0.5, 0.78125, 0, 0.71875, 0, 0, 0, 0.453125, 0, 0, 0, 0, 0.421875]","[0.8, 0.40629512, 0.70751506, 0.6557804, 0.76947165, 0.0, 0.52402824, 0.8, 0.015851827, 0.7253554, 0.0, 0.0, 0.0, 0.454415, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1962,76.24,0.7624,20279808,50.01136363636364,71.35892953200646,adc_episode_1962,"[0.75, 0.625, 0.75, 0.6875, 0.5625, 0.375, 0, 0, 0.6875, 0, 0.3125, 0.375, 0, 0, 0, 0, 0, 0.3125, 0.71875]","[0.76483124, 0.66466063, 0.77826256, 0.68935025, 0.5841711, 0.40286183, 0.0, 0.0, 0.704627, 0.0, 0.31971213, 0.4057014, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1963,69.66,0.6966,20277504,50.00568181818181,61.19889725658957,adc_episode_1963,"[0.25, 0.75, 0.75, 0.375, 0.25, 0.75, 0, 0.375, 0.4375, 0.21875, 0.09375, 0, 0, 0, 0, 0, 0.46875, 0.625, 0.484375]","[0.25475612, 0.8, 0.8, 0.4292778, 0.30996284, 0.8, 0.0, 0.40112513, 0.43821472, 0.22551078, 0.12038084, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2676682, 0.0]"
-1964,74.22999999999999,0.7422999999999998,20279808,50.01136363636364,64.37937062937063,adc_episode_1964,"[0.625, 0.5, 0.75, 0.75, 0.5625, 0.5, 0, 0.375, 0.0625, 0.15625, 0.09375, 0.3125, 0.1875, 0, 0, 0, 0.109375, 0.78125, 0.109375]","[0.65856224, 0.52241, 0.7879612, 0.8, 0.59464556, 0.5576161, 0.0, 0.38090375, 0.087683685, 0.15754408, 0.12310815, 0.33809495, 0.19704215, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1965,66.94,0.6694,20275200,50.0,67.56993006993007,adc_episode_1965,"[0.6875, 0.25, 0.75, 0.75, 0.25, 0.4375, 0, 0.78125, 0.78125, 0.1875, 0, 0.1875, 0, 0, 0, 0, 0, 0.578125, 0.5625]","[0.7105368, 0.25554872, 0.8, 0.8, 0.27463663, 0.48033145, 0.037997797, 0.8, 0.8, 0.18986927, 0.0, 0.18759988, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1966,54.87,0.5487,20289024,50.034090909090914,57.070333512641206,adc_episode_1966,"[0.4375, 0.75, 0.4375, 0.75, 0.75, 0, 0, 0.34375, 0.6875, 0.15625, 0, 0, 0, 0, 0, 0.03125, 0.734375, 0.28125, 0.71875]","[0.474671, 0.8, 0.46992812, 0.8, 0.78191924, 0.0, 0.0, 0.36371934, 0.7039848, 0.17416479, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03361907, 0.0, 0.18754025, 0.0]"
-1967,61.18,0.6118,20278656,50.008522727272734,64.32221624529318,adc_episode_1967,"[0.625, 0.75, 0.75, 0.625, 0.625, 0, 0.75, 0, 0.625, 0, 0, 0, 0, 0, 0, 0, 0.265625, 0.71875, 0.3125]","[0.6320499, 0.8, 0.8, 0.6682638, 0.6568099, 0.0, 0.77933574, 0.0, 0.63690895, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1968,72.89,0.7289,20307456,50.07954545454546,74.80836471221086,adc_episode_1968,"[0.6875, 0.625, 0.75, 0.75, 0.5625, 0, 0.4375, 0.21875, 0.5625, 0.125, 0, 0.65625, 0.21875, 0, 0, 0, 0, 0, 0.65625]","[0.7320202, 0.62971807, 0.8, 0.8, 0.60981655, 0.0, 0.48848835, 0.22428448, 0.58987623, 0.12633395, 0.0, 0.6733828, 0.24046566, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1969,61.24000000000001,0.6124,20289024,50.034090909090914,67.93302850995158,adc_episode_1969,"[0.75, 0.5, 0.375, 0.75, 0.75, 0.25, 0.0625, 0.6875, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.59375, 0.5]","[0.8, 0.52949667, 0.39433643, 0.8, 0.8, 0.28392175, 0.11186286, 0.70742816, 0.027551841, 0.7908297, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1970,70.34,0.7034,20276928,50.00426136363636,59.188407746100054,adc_episode_1970,"[0.25, 0.75, 0.75, 0.25, 0.75, 0.3125, 0, 0.78125, 0.3125, 0, 0, 0.125, 0, 0, 0, 0.203125, 0.265625, 0.734375, 0.265625]","[0.28473914, 0.8, 0.8, 0.26389974, 0.8, 0.34248722, 0.0, 0.8, 0.33334982, 0.0, 0.0, 0.14094149, 0.0, 0.0, 0.0, 0.21592338, 0.06483628, 0.40378237, 0.0]"
-1971,74.82,0.7482,20282112,50.01704545454545,74.70077998924152,adc_episode_1971,"[0.75, 0.6875, 0.75, 0.3125, 0.625, 0.4375, 0, 0.3125, 0, 0.5, 0.15625, 0.71875, 0, 0, 0, 0, 0, 0, 0.78125]","[0.8, 0.70593876, 0.8, 0.347631, 0.6436797, 0.49671382, 0.0, 0.31318697, 0.01714124, 0.5082595, 0.1835196, 0.7193619, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1972,66.73,0.6673,20309760,50.08522727272727,74.90250134480904,adc_episode_1972,"[0.75, 0.75, 0.5, 0.1875, 0.75, 0, 0.5625, 0.59375, 0.78125, 0.53125, 0, 0.21875, 0, 0, 0, 0, 0, 0, 0.734375]","[0.8, 0.8, 0.50437987, 0.24226326, 0.8, 0.0, 0.5849997, 0.61650646, 0.8, 0.55109346, 0.0, 0.24500251, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1973,71.45,0.7145,20065536,49.48295454545455,67.05217859064014,adc_episode_1973,"[0.5625, 0.5, 0.75, 0.25, 0.375, 0.6875, 0, 0.3125, 0.78125, 0, 0.0625, 0.34375, 0.09375, 0, 0, 0, 0, 0.5, 0.796875]","[0.5775208, 0.5598713, 0.8, 0.28436986, 0.4093504, 0.745566, 0.0, 0.31827316, 0.8, 0.0, 0.08263258, 0.3609782, 0.097973324, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8]"
-1974,69.58,0.6958,20293632,50.04545454545455,72.91218396987628,adc_episode_1974,"[0.4375, 0.75, 0.75, 0.75, 0.1875, 0.75, 0, 0.625, 0, 0.25, 0, 0.46875, 0, 0, 0, 0, 0, 0.25, 0.734375]","[0.4705095, 0.8, 0.8, 0.8, 0.21729629, 0.8, 0.0, 0.62944925, 0.0, 0.28036493, 0.0, 0.47308975, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12772448, 0.0]"
-1975,63.6,0.636,20279808,50.01136363636364,64.00618612157074,adc_episode_1975,"[0.3125, 0.5625, 0.75, 0.75, 0.125, 0.75, 0.3125, 0.25, 0.46875, 0, 0, 0.21875, 0, 0, 0, 0.09375, 0.078125, 0.78125, 0.125]","[0.3202451, 0.60468155, 0.8, 0.8, 0.15567164, 0.8, 0.3494308, 0.26110604, 0.4749964, 0.0, 0.0, 0.23755358, 0.0, 0.0, 0.0, 0.10385383, 0.0, 0.0, 0.037833747]"
-1976,65.8,0.6579999999999999,20284416,50.02272727272727,69.41904249596558,adc_episode_1976,"[0.125, 0.75, 0.625, 0.625, 0.125, 0.75, 0, 0.40625, 0.6875, 0.625, 0, 0, 0, 0, 0, 0, 0, 0.484375, 0.625]","[0.16752179, 0.8, 0.6367246, 0.63779074, 0.17056088, 0.8, 0.0, 0.43115684, 0.7000552, 0.6403741, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1977,67.67,0.6767,20277504,50.00568181818181,66.2015868746638,adc_episode_1977,"[0.4375, 0.75, 0.75, 0.375, 0.75, 0, 0, 0.78125, 0.125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.6875, 0.359375]","[0.4768296, 0.8, 0.8, 0.41404894, 0.8, 0.0, 0.057403807, 0.8, 0.14943732, 0.7837885, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03791125, 0.123697825]"
-1978,68.10000000000001,0.681,20277504,50.00568181818181,65.67711135018828,adc_episode_1978,"[0.5, 0.25, 0.75, 0, 0.75, 0.4375, 0.125, 0.59375, 0.59375, 0, 0.1875, 0.28125, 0, 0, 0, 0, 0, 0.75, 0.234375]","[0.557058, 0.30454814, 0.8, 0.02281379, 0.8, 0.45277518, 0.17951569, 0.6061382, 0.6219074, 0.016709736, 0.20014064, 0.2835997, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1979,66.03999999999999,0.6603999999999999,20278080,50.00710227272728,64.59117805271651,adc_episode_1979,"[0.375, 0.75, 0.6875, 0.75, 0.75, 0, 0.4375, 0.53125, 0.15625, 0.03125, 0.375, 0, 0, 0.1875, 0, 0, 0, 0.703125, 0.328125]","[0.43002242, 0.8, 0.7084739, 0.8, 0.8, 0.0, 0.49284488, 0.5573276, 0.16023156, 0.03708783, 0.40170792, 0.007613543, 0.0, 0.19396763, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1980,74.09,0.7409,20275200,50.0,71.78590640129102,adc_episode_1980,"[0.4375, 0.6875, 0.75, 0.5625, 0, 0.75, 0, 0.1875, 0.5, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0.25, 0.75]","[0.46727166, 0.7156972, 0.8, 0.6143544, 0.0, 0.8, 0.0, 0.19206545, 0.5000291, 0.0, 0.8, 0.0109780505, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1981,68.16,0.6816,20293632,50.04545454545455,76.75497579343732,adc_episode_1981,"[0.75, 0.375, 0.75, 0.75, 0.6875, 0.125, 0.25, 0, 0.78125, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.578125]","[0.8, 0.38693756, 0.8, 0.8, 0.7316099, 0.12666045, 0.28161776, 0.0, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1982,66.75999999999999,0.6675999999999999,20285568,50.02556818181818,69.4055944055944,adc_episode_1982,"[0.75, 0.75, 0.75, 0.75, 0.6875, 0, 0.375, 0.3125, 0.78125, 0.125, 0, 0.125, 0, 0, 0, 0, 0, 0.53125, 0.578125]","[0.8, 0.8, 0.8, 0.8, 0.7475326, 0.0, 0.41451028, 0.3301918, 0.8, 0.14409629, 0.0, 0.12965368, 0.0, 0.0, 0.0, 0.0, 0.0, 0.32532078, 0.0]"
-1983,78.35,0.7835,20287296,50.02982954545454,69.56697149004842,adc_episode_1983,"[0.625, 0.1875, 0.75, 0.4375, 0.625, 0.375, 0.3125, 0.59375, 0, 0.625, 0.40625, 0, 0, 0, 0, 0, 0, 0.421875, 0.671875]","[0.67144966, 0.24253662, 0.8, 0.45814928, 0.6255928, 0.42392853, 0.3581609, 0.59540236, 0.0, 0.65423304, 0.41209462, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0009894501, 0.0, 0.0]"
-1984,49.99,0.4999,20300544,50.0625,82.55446476600324,adc_episode_1984,"[0.75, 0.75, 0.75, 0.4375, 0, 0.6875, 0, 0.21875, 0.65625, 0, 0.75, 0.71875, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.4442507, 0.0, 0.7414542, 0.0, 0.21999002, 0.6836321, 0.0, 0.7770765, 0.7226155, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1985,63.22,0.6322,20302848,50.06818181818182,77.54505110274341,adc_episode_1985,"[0.75, 0.625, 0.5625, 0.75, 0.5625, 0.1875, 0, 0.78125, 0.75, 0, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0.4375]","[0.8, 0.6268295, 0.61880887, 0.8, 0.5995117, 0.22526349, 0.036868244, 0.78981286, 0.7666487, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1986,60.19,0.6019,20276928,50.00426136363636,67.82208176438945,adc_episode_1986,"[0.25, 0.75, 0.75, 0.75, 0.75, 0.5, 0, 0.125, 0.78125, 0.3125, 0, 0, 0, 0, 0, 0, 0, 0.703125, 0.359375]","[0.29311395, 0.8, 0.8, 0.8, 0.8, 0.5306376, 0.0, 0.14356832, 0.8, 0.31612086, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1987,73.25999999999999,0.7325999999999999,20278656,50.008522727272734,63.11188811188811,adc_episode_1987,"[0.4375, 0.75, 0.75, 0.375, 0.625, 0.5625, 0, 0.15625, 0.53125, 0, 0.28125, 0, 0, 0, 0, 0, 0.328125, 0.703125, 0.328125]","[0.4770579, 0.8, 0.8, 0.38833222, 0.6858725, 0.6034225, 0.0, 0.15739374, 0.55677485, 0.0, 0.28387213, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.052405406]"
-1988,59.150000000000006,0.5915,20280960,50.01420454545455,66.1208983324368,adc_episode_1988,"[0.6875, 0.75, 0.5, 0.375, 0.625, 0.75, 0, 0.125, 0.125, 0.5625, 0.28125, 0, 0, 0, 0, 0, 0.0625, 0.796875, 0.03125]","[0.68938476, 0.8, 0.54598624, 0.42655456, 0.67234176, 0.7524602, 0.0, 0.14387518, 0.15173355, 0.58240336, 0.29806045, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1989,51.910000000000004,0.5191,20310336,50.08664772727273,77.12479827864443,adc_episode_1989,"[0.75, 0.75, 0.5625, 0.75, 0.75, 0, 0.25, 0.78125, 0.09375, 0.3125, 0.78125, 0, 0, 0.203125, 0.078125, 0, 0, 0, 0.109375]","[0.8, 0.8, 0.5625297, 0.8, 0.8, 0.0, 0.27201292, 0.8, 0.10504352, 0.31836826, 0.8, 0.0, 0.0, 0.21406454, 0.09178876, 0.0, 0.0, 0.0, 0.0]"
-1990,66.52,0.6652,20282112,50.01704545454545,69.22404518558365,adc_episode_1990,"[0.625, 0.75, 0.375, 0.75, 0.5, 0.5625, 0, 0, 0.78125, 0, 0.375, 0, 0, 0, 0, 0, 0, 0.546875, 0.5625]","[0.6732028, 0.8, 0.43395606, 0.8, 0.54275656, 0.6218998, 0.0, 0.0, 0.8, 0.023724435, 0.38913703, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08819187]"
-1991,62.480000000000004,0.6248,20275200,50.0,75.45723507261968,adc_episode_1991,"[0.5, 0.625, 0.75, 0, 0.75, 0.5625, 0, 0, 0.78125, 0, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0.703125]","[0.51081777, 0.6304902, 0.8, 0.0, 0.8, 0.6011785, 0.0, 0.0, 0.8, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1992,56.00000000000001,0.56,20276352,50.00284090909091,60.97027972027972,adc_episode_1992,"[0.75, 0.4375, 0.5625, 0.75, 0, 0.75, 0, 0, 0.5625, 0.28125, 0, 0, 0.03125, 0.03125, 0, 0, 0.46875, 0.625, 0.484375]","[0.8, 0.47361967, 0.60771763, 0.8, 0.0, 0.8, 0.0, 0.0, 0.57166475, 0.30153966, 0.0, 0.0, 0.06121487, 0.04141619, 0.0, 0.0, 0.0, 0.22613442, 0.2718561]"
-1993,68.34,0.6834,20293056,50.04403409090909,74.96637977407208,adc_episode_1993,"[0.375, 0.6875, 0.75, 0.75, 0.0625, 0.75, 0, 0.6875, 0.09375, 0.09375, 0.40625, 0.40625, 0, 0, 0, 0, 0, 0.015625, 0.796875]","[0.41948563, 0.7056321, 0.8, 0.8, 0.066505454, 0.8, 0.0, 0.71565855, 0.11996642, 0.12406544, 0.4262731, 0.41473088, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35651144]"
-1994,56.37,0.5637,20304000,50.07102272727273,72.66003227541688,adc_episode_1994,"[0.6875, 0.6875, 0.5625, 0.75, 0.125, 0.4375, 0.5, 0.78125, 0.78125, 0, 0.3125, 0, 0, 0, 0, 0, 0.046875, 0.09375, 0.78125]","[0.7179336, 0.69821477, 0.5989156, 0.8, 0.17417447, 0.45577735, 0.561926, 0.8, 0.8, 0.0, 0.34260193, 0.0, 0.0, 0.0, 0.0, 0.0, 0.059742395, 0.0, 0.0]"
-1995,67.78,0.6778,20287296,50.02982954545454,61.01398601398601,adc_episode_1995,"[0.75, 0.75, 0.75, 0.4375, 0.5625, 0, 0.75, 0, 0.46875, 0, 0, 0.09375, 0, 0.15625, 0, 0.234375, 0, 0.640625, 0.453125]","[0.8, 0.7617885, 0.8, 0.4731948, 0.56412005, 0.0, 0.8, 0.0, 0.48807442, 0.0, 0.0, 0.117962666, 0.0, 0.16722056, 0.0010586325, 0.24218509, 0.0, 0.16860496, 0.0]"
-1996,60.68,0.6068,20284416,50.02272727272727,61.713286713286706,adc_episode_1996,"[0.4375, 0.75, 0.75, 0.75, 0.75, 0, 0, 0.34375, 0.78125, 0, 0.0625, 0.09375, 0, 0, 0, 0, 0.359375, 0.703125, 0.328125]","[0.4419486, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.3592187, 0.8, 0.0, 0.083674185, 0.1070336, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3594468, 0.0]"
-1997,68.7,0.687,20290176,50.03693181818182,73.81993006993007,adc_episode_1997,"[0, 0.75, 0.5625, 0.75, 0.75, 0, 0.75, 0, 0.78125, 0.21875, 0, 0.25, 0, 0, 0, 0, 0, 0.109375, 0.78125]","[0.021150311, 0.8, 0.5800058, 0.8, 0.7530135, 0.0, 0.8, 0.0, 0.8, 0.24898678, 0.0, 0.27539477, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-1998,71.67999999999999,0.7167999999999999,20282688,50.018465909090914,70.36713286713287,adc_episode_1998,"[0.75, 0.75, 0.125, 0.75, 0.125, 0.75, 0, 0, 0.78125, 0, 0, 0.34375, 0.21875, 0, 0.140625, 0, 0, 0.046875, 0.796875]","[0.8, 0.8, 0.1391301, 0.8, 0.14530358, 0.8, 0.0, 0.02310771, 0.8, 0.0, 0.0050964886, 0.34468797, 0.24853554, 0.0, 0.14499809, 0.0, 0.0, 0.0, 0.0]"
-1999,72.09,0.7209,20275776,50.00142045454545,67.06898870360409,adc_episode_1999,"[0.4375, 0.625, 0.75, 0.75, 0.5625, 0, 0.3125, 0.59375, 0, 0.78125, 0.125, 0, 0, 0, 0, 0, 0, 0.640625, 0.453125]","[0.438583, 0.6401176, 0.8, 0.79709, 0.5899487, 0.0, 0.3669653, 0.6191715, 0.0, 0.8, 0.14817868, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2000,67.04,0.6704000000000001,20275200,50.0,65.53254437869822,adc_episode_2000,"[0.75, 0.625, 0.75, 0.75, 0.3125, 0.75, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0.1875, 0.75, 0.25]","[0.8, 0.6376767, 0.8, 0.7830653, 0.35952297, 0.8, 0.0, 0.013275261, 0.7673469, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2837578, 0.0]"
-2001,61.85000000000001,0.6185,20286720,50.028409090909086,73.20467993544916,adc_episode_2001,"[0.75, 0.75, 0.75, 0.6875, 0.75, 0.625, 0, 0.03125, 0, 0.125, 0.6875, 0.40625, 0, 0, 0, 0, 0, 0.28125, 0.71875]","[0.8, 0.7999661, 0.7730463, 0.7283078, 0.8, 0.65614235, 0.0, 0.04595697, 0.028601917, 0.13667168, 0.6902321, 0.43543628, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2002,67.72,0.6772,20289024,50.034090909090914,68.88111888111888,adc_episode_2002,"[0.75, 0.75, 0.375, 0.75, 0.0625, 0.5, 0, 0.78125, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.484375, 0.625]","[0.8, 0.7664596, 0.43087003, 0.8, 0.09024083, 0.5581751, 0.0, 0.8, 0.0, 0.7869109, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1109361]"
-2003,69.36,0.6936,20286720,50.028409090909086,67.57665411511566,adc_episode_2003,"[0.625, 0.75, 0.75, 0.5625, 0.5, 0.125, 0.375, 0.03125, 0.28125, 0.34375, 0.65625, 0.03125, 0, 0, 0, 0, 0, 0.640625, 0.4375]","[0.6818052, 0.7648992, 0.8, 0.5939675, 0.5131714, 0.16907829, 0.4372206, 0.043096557, 0.3065835, 0.37356508, 0.67626446, 0.044463262, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2004,60.63000000000001,0.6063000000000001,20282112,50.01704545454545,62.66810112963959,adc_episode_2004,"[0.75, 0.5, 0.75, 0.75, 0.6875, 0.75, 0, 0.03125, 0.5, 0, 0, 0, 0.03125, 0, 0.34375, 0, 0, 0.59375, 0.53125]","[0.8, 0.53284144, 0.8, 0.8, 0.70021164, 0.75690323, 0.0, 0.040010512, 0.5134016, 0.0, 0.01224977, 0.027655866, 0.050497383, 0.0, 0.3539956, 0.0, 0.0, 0.0, 0.013167451]"
-2005,62.53999999999999,0.6254,20281536,50.015625,56.79464766003227,adc_episode_2005,"[0.75, 0.5625, 0.75, 0.3125, 0.75, 0, 0.75, 0, 0, 0.21875, 0.0625, 0, 0, 0, 0.0625, 0.0625, 0.78125, 0.140625, 0.765625]","[0.8, 0.6017209, 0.8, 0.33788684, 0.8, 0.0, 0.76331943, 0.0, 0.0, 0.24274318, 0.07532444, 0.0, 0.0, 0.0, 0.06342489, 0.0, 0.0, 0.0, 0.15804803]"
-2006,72.83,0.7283,20289024,50.034090909090914,70.12170521785906,adc_episode_2006,"[0.6875, 0.0625, 0.75, 0.6875, 0.75, 0.5, 0, 0.78125, 0, 0.40625, 0.34375, 0, 0, 0, 0, 0, 0, 0.4375, 0.640625]","[0.7417669, 0.12073206, 0.8, 0.7423595, 0.8, 0.5095894, 0.0, 0.8, 0.0, 0.4172964, 0.35668093, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2007,74.22,0.7422,20286720,50.028409090909086,68.41379774072081,adc_episode_2007,"[0.75, 0.3125, 0.75, 0.5, 0.75, 0, 0, 0.375, 0.3125, 0.53125, 0.71875, 0, 0, 0, 0, 0, 0, 0.46875, 0.625]","[0.8, 0.35511515, 0.76518446, 0.5558381, 0.8, 0.0, 0.0, 0.39444986, 0.3135583, 0.55396706, 0.7320194, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3011246, 0.35026142]"
-2008,74.4,0.7440000000000001,20286144,50.02698863636363,69.73171059709522,adc_episode_2008,"[0.5, 0.75, 0.5, 0.75, 0.4375, 0.25, 0, 0, 0.59375, 0.1875, 0, 0.78125, 0, 0, 0, 0, 0, 0.359375, 0.703125]","[0.55266434, 0.8, 0.50255156, 0.8, 0.4749508, 0.29767928, 0.0, 0.024486428, 0.6184158, 0.20507075, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10361975]"
-2009,70.26,0.7026,20279232,50.00994318181819,68.36000537923614,adc_episode_2009,"[0.75, 0, 0.75, 0.75, 0.625, 0.5625, 0, 0.3125, 0.0625, 0, 0.78125, 0.125, 0, 0, 0, 0, 0, 0.609375, 0.515625]","[0.8, 0.027350672, 0.8, 0.8, 0.63131064, 0.5665528, 0.0, 0.33284184, 0.08829511, 0.016243955, 0.8, 0.15533178, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.51057005]"
-2010,75.11,0.7511,20275200,50.0,67.21691769768692,adc_episode_2010,"[0.5625, 0.75, 0.75, 0.3125, 0.75, 0, 0.375, 0, 0.40625, 0.375, 0.21875, 0, 0.4375, 0, 0, 0, 0, 0.46875, 0.625]","[0.60789657, 0.8, 0.78357536, 0.3675605, 0.8, 0.0, 0.41309792, 0.0, 0.43097305, 0.38871074, 0.23638864, 0.0, 0.44415224, 0.0, 0.0, 0.0, 0.0, 0.33240873, 0.51296985]"
-2011,65.94,0.6594,20287872,50.03125000000001,61.25941366325982,adc_episode_2011,"[0.5625, 0.125, 0.75, 0.75, 0.625, 0, 0, 0.78125, 0, 0.78125, 0, 0, 0, 0.375, 0, 0, 0, 0.515625, 0.59375]","[0.58003867, 0.13014863, 0.8, 0.8, 0.6468131, 0.014244264, 0.0, 0.8, 0.0, 0.8, 0.0, 0.0, 0.0, 0.38021013, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2012,70.92,0.7092,20283264,50.01988636363637,60.88959117805272,adc_episode_2012,"[0.75, 0.75, 0.75, 0.625, 0.5, 0.25, 0, 0.34375, 0.40625, 0.28125, 0.15625, 0, 0, 0, 0, 0, 0.46875, 0.640625, 0.4375]","[0.7813259186057734, 0.7668665221949532, 0.8, 0.6845134108111168, 0.5485949966114494, 0.2802360772056635, 0.05347908158702548, 0.36560038276080575, 0.4357590055518377, 0.2879589500225738, 0.17169966009863952, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2013,69.29,0.6929000000000001,20283264,50.01988636363637,61.756993006993014,adc_episode_2013,"[0.75, 0.6875, 0.75, 0.6875, 0.4375, 0.25, 0.0625, 0.375, 0.40625, 0.1875, 0.25, 0, 0, 0, 0, 0, 0.390625, 0.671875, 0.390625]","[0.7988330117934905, 0.714214021034676, 0.7886993800097536, 0.7019447214091072, 0.4958413805207258, 0.2595925502291442, 0.12498611933245354, 0.39680399027497804, 0.42440316298985215, 0.21138304607657132, 0.262920037338097, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2014,71.36,0.7136,20277504,50.00568181818181,61.208983324367935,adc_episode_2014,"[0.6875, 0.75, 0.6875, 0.625, 0.5, 0.3125, 0, 0.375, 0.40625, 0.21875, 0.1875, 0, 0, 0, 0, 0, 0.4375, 0.65625, 0.4375]","[0.7123832699937092, 0.7616880494178638, 0.7103091919812445, 0.674372871057765, 0.5509847860843146, 0.3192269297689845, 0.00020488322554018613, 0.39648012053084275, 0.41741946584385464, 0.22065195536137458, 0.20093895215287974, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2015,75.18,0.7518,20283264,50.01988636363637,62.49327595481442,adc_episode_2015,"[0.75, 0.6875, 0.75, 0.625, 0.5625, 0.0625, 0.25, 0.34375, 0.4375, 0.25, 0.09375, 0.09375, 0, 0, 0, 0, 0.3125, 0.71875, 0.296875]","[0.8, 0.7297254081341106, 0.7567097435820834, 0.6871873048131477, 0.5992161943435871, 0.0738485688242912, 0.2701744758934511, 0.361762695117618, 0.43784541589347553, 0.2508955417137234, 0.1056663515514132, 0.09800983457993441, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2016,70.19,0.7019,20280960,50.01420454545455,60.95683162990855,adc_episode_2016,"[0.6875, 0.75, 0.6875, 0.625, 0.5625, 0.0625, 0.3125, 0.3125, 0.4375, 0.21875, 0.125, 0, 0, 0, 0, 0, 0.46875, 0.640625, 0.4375]","[0.695064289510902, 0.8, 0.7497465042291861, 0.6696517762087437, 0.5776410197587241, 0.11400283768054106, 0.35156612040261676, 0.32554654363965246, 0.4480270535174607, 0.24618150043638476, 0.15409138604874384, 0.0016759373520368933, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2017,68.05,0.6805,20283264,50.01988636363637,60.476062399139316,adc_episode_2017,"[0.375, 0.75, 0.6875, 0.75, 0.625, 0, 0, 0.25, 0.6875, 0, 0.34375, 0, 0, 0, 0.03125, 0.03125, 0.3125, 0.71875, 0.296875]","[0.43437582, 0.8, 0.6906361, 0.7554879, 0.66159004, 0.0, 0.0, 0.25757545, 0.710949, 0.0, 0.3710164, 0.00039752468, 0.0, 0.0, 0.041891746, 0.03903785, 0.0, 0.0, 0.0]"
-2018,56.13,0.5613,20275200,50.0,62.2545723507262,adc_episode_2018,"[0.0625, 0.5, 0.75, 0.1875, 0.75, 0.0625, 0.75, 0, 0.78125, 0, 0, 0, 0.1875, 0, 0, 0, 0.21875, 0.75, 0.234375]","[0.12460622, 0.5223602, 0.8, 0.23558892, 0.8, 0.08445027, 0.7961495, 0.0, 0.8, 0.0, 0.027907718, 0.0, 0.20209166, 0.0, 0.0, 0.0, 0.17435335, 0.0, 0.0]"
-2019,65.03999999999999,0.6503999999999999,20256192,49.953125,64.3928187197418,adc_episode_2019,"[0.375, 0.75, 0, 0.4375, 0.75, 0.0625, 0.75, 0, 0.625, 0, 0.3125, 0, 0.15625, 0, 0, 0, 0.015625, 0.796875, 0.0625]","[0.40267208, 0.8, 0.0, 0.45518067, 0.8, 0.07073586, 0.8, 0.0, 0.64334327, 0.0, 0.33884704, 0.0, 0.18417065, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07569972]"
-2020,69.52000000000001,0.6952000000000002,20282112,50.01704545454545,62.73870360408822,adc_episode_2020,"[0, 0.6875, 0.75, 0.375, 0.5625, 0.5, 0.1875, 0.0625, 0.21875, 0.78125, 0, 0, 0.15625, 0, 0, 0, 0.203125, 0.75, 0.203125]","[0.033575784, 0.7094713, 0.8, 0.40989986, 0.6140757, 0.50305665, 0.22027513, 0.08565014, 0.22176738, 0.8, 0.0, 0.0, 0.15707183, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-2021,67.32000000000001,0.6732,20275200,50.0,79.41097364174287,adc_episode_2021,"[0.75, 0.375, 0.75, 0.0625, 0.75, 0, 0.6875, 0, 0.78125, 0, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0.328125]","[0.8, 0.37768987, 0.76704496, 0.08187187, 0.8, 0.0, 0.6947548, 0.0, 0.8, 0.02459995, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3178,47.78,0.4778,20281536,50.015625,69.0794782140936,adc_episode_3178,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3181,49.980000000000004,0.4998,20281536,50.015625,69.0794782140936,adc_episode_3181,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3184,47.48,0.47479999999999994,20281536,50.015625,69.0794782140936,adc_episode_3184,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3185,47.11,0.4711,20281536,50.015625,69.0794782140936,adc_episode_3185,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3186,46.77,0.4677,20281536,50.015625,69.0794782140936,adc_episode_3186,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3187,47.84,0.47840000000000005,20281536,50.015625,69.0794782140936,adc_episode_3187,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3188,42.410000000000004,0.42410000000000003,20281536,50.015625,69.0794782140936,adc_episode_3188,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3189,38.63999999999999,0.3863999999999999,20281536,50.015625,69.0794782140936,adc_episode_3189,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3190,47.78,0.4778,20281536,50.015625,69.0794782140936,adc_episode_3190,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3191,43.720000000000006,0.43720000000000003,20281536,50.015625,69.0794782140936,adc_episode_3191,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3192,49.95,0.49950000000000006,20281536,50.015625,69.0794782140936,adc_episode_3192,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3193,51.49,0.5149,20281536,50.015625,69.0794782140936,adc_episode_3193,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3194,50.0,0.5,20281536,50.015625,69.0794782140936,adc_episode_3194,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3195,49.99,0.4999,20281536,50.015625,69.0794782140936,adc_episode_3195,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3196,44.589999999999996,0.44589999999999996,20281536,50.015625,69.0794782140936,adc_episode_3196,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3197,54.08,0.5408,20281536,50.015625,69.0794782140936,adc_episode_3197,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3199,49.760000000000005,0.49760000000000004,20281536,50.015625,69.0794782140936,adc_episode_3199,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3200,40.59,0.40590000000000004,20281536,50.015625,69.0794782140936,adc_episode_3200,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3201,51.18000000000001,0.5118,20281536,50.015625,69.0794782140936,adc_episode_3201,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3202,40.73,0.4073,20281536,50.015625,69.0794782140936,adc_episode_3202,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3203,41.059999999999995,0.41059999999999997,20281536,50.015625,69.0794782140936,adc_episode_3203,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3204,52.67999999999999,0.5267999999999999,20281536,50.015625,69.0794782140936,adc_episode_3204,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3205,43.82,0.4382,20281536,50.015625,69.0794782140936,adc_episode_3205,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3206,46.540000000000006,0.46540000000000004,20281536,50.015625,69.0794782140936,adc_episode_3206,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3207,45.67,0.4567,20281536,50.015625,69.0794782140936,adc_episode_3207,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3208,43.03,0.4303,20281536,50.015625,69.0794782140936,adc_episode_3208,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3209,47.41,0.47409999999999997,20281536,50.015625,69.0794782140936,adc_episode_3209,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3332,41.82,0.4182,20281536,50.015625,69.0794782140936,adc_episode_3332,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625, 0.171875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3370,43.8,0.43799999999999994,19496448,48.07954545454545,84.16487358795051,adc_episode_3370,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3372,37.63999999999999,0.37639999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3372,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3376,41.349999999999994,0.4134999999999999,19496448,48.07954545454545,84.16487358795051,adc_episode_3376,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3377,39.290000000000006,0.3929000000000001,19496448,48.07954545454545,84.16487358795051,adc_episode_3377,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3382,32.330000000000005,0.32330000000000003,19496448,48.07954545454545,84.16487358795051,adc_episode_3382,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3385,40.910000000000004,0.4091,19496448,48.07954545454545,84.16487358795051,adc_episode_3385,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3386,29.069999999999997,0.29069999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3386,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3387,27.459999999999994,0.27459999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3387,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3388,40.86,0.4086,19496448,48.07954545454545,84.16487358795051,adc_episode_3388,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3389,43.55,0.4355,19496448,48.07954545454545,84.16487358795051,adc_episode_3389,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3390,33.919999999999995,0.33919999999999995,19496448,48.07954545454545,84.16487358795051,adc_episode_3390,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3391,39.300000000000004,0.393,19496448,48.07954545454545,84.16487358795051,adc_episode_3391,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3392,35.440000000000005,0.35440000000000005,19496448,48.07954545454545,84.16487358795051,adc_episode_3392,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3393,42.669999999999995,0.42669999999999997,19496448,48.07954545454545,84.16487358795051,adc_episode_3393,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3394,44.620000000000005,0.44620000000000004,19496448,48.07954545454545,84.16487358795051,adc_episode_3394,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3395,34.47,0.3447,19496448,48.07954545454545,84.16487358795051,adc_episode_3395,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3396,46.160000000000004,0.4616,19496448,48.07954545454545,84.16487358795051,adc_episode_3396,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3397,37.35000000000001,0.3735000000000001,19496448,48.07954545454545,84.16487358795051,adc_episode_3397,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3398,37.129999999999995,0.37129999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3398,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3399,41.05,0.4105,19496448,48.07954545454545,84.16487358795051,adc_episode_3399,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3400,42.15,0.4215,19496448,48.07954545454545,84.16487358795051,adc_episode_3400,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3401,33.879999999999995,0.33879999999999993,19496448,48.07954545454545,84.16487358795051,adc_episode_3401,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3402,39.71,0.3971,19496448,48.07954545454545,84.16487358795051,adc_episode_3402,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3403,32.489999999999995,0.32489999999999997,19496448,48.07954545454545,84.16487358795051,adc_episode_3403,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3404,37.39,0.3739,19496448,48.07954545454545,84.16487358795051,adc_episode_3404,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3405,30.700000000000006,0.30700000000000005,19496448,48.07954545454545,84.16487358795051,adc_episode_3405,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3406,34.48,0.3448,19496448,48.07954545454545,84.16487358795051,adc_episode_3406,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3407,43.93,0.4393,19496448,48.07954545454545,84.16487358795051,adc_episode_3407,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3408,45.84,0.45840000000000003,19496448,48.07954545454545,84.16487358795051,adc_episode_3408,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3409,40.22,0.4022,19496448,48.07954545454545,84.16487358795051,adc_episode_3409,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3410,36.519999999999996,0.36519999999999997,19496448,48.07954545454545,84.16487358795051,adc_episode_3410,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3411,39.0,0.39,19496448,48.07954545454545,84.16487358795051,adc_episode_3411,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3412,40.2,0.402,19496448,48.07954545454545,84.16487358795051,adc_episode_3412,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3413,37.629999999999995,0.37629999999999997,19496448,48.07954545454545,84.16487358795051,adc_episode_3413,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3414,45.04,0.45039999999999997,19496448,48.07954545454545,84.16487358795051,adc_episode_3414,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3415,42.92,0.4292,19496448,48.07954545454545,84.16487358795051,adc_episode_3415,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3416,30.769999999999996,0.3077,19496448,48.07954545454545,84.16487358795051,adc_episode_3416,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3417,23.729999999999997,0.23729999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3417,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3418,36.129999999999995,0.36129999999999995,19496448,48.07954545454545,84.16487358795051,adc_episode_3418,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3419,43.68,0.4368,19496448,48.07954545454545,84.16487358795051,adc_episode_3419,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3420,32.58,0.3258,19496448,48.07954545454545,84.16487358795051,adc_episode_3420,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3421,44.97,0.4497,19496448,48.07954545454545,84.16487358795051,adc_episode_3421,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3422,42.84,0.42840000000000006,19496448,48.07954545454545,84.16487358795051,adc_episode_3422,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3423,44.85,0.4485,19496448,48.07954545454545,84.16487358795051,adc_episode_3423,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3424,41.949999999999996,0.4195,19496448,48.07954545454545,84.16487358795051,adc_episode_3424,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3425,36.1,0.361,19496448,48.07954545454545,84.16487358795051,adc_episode_3425,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3426,42.02,0.4202,19496448,48.07954545454545,84.16487358795051,adc_episode_3426,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3427,39.44,0.3944,19496448,48.07954545454545,84.16487358795051,adc_episode_3427,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3428,29.079999999999995,0.29079999999999995,19496448,48.07954545454545,84.16487358795051,adc_episode_3428,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3429,35.79,0.3579,19496448,48.07954545454545,84.16487358795051,adc_episode_3429,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3430,42.00000000000001,0.4200000000000001,19496448,48.07954545454545,84.16487358795051,adc_episode_3430,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3431,38.99,0.3899,19496448,48.07954545454545,84.16487358795051,adc_episode_3431,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3432,41.1,0.41100000000000003,19496448,48.07954545454545,84.16487358795051,adc_episode_3432,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3433,36.24000000000001,0.3624000000000001,19496448,48.07954545454545,84.16487358795051,adc_episode_3433,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3434,40.38,0.40380000000000005,19496448,48.07954545454545,84.16487358795051,adc_episode_3434,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3435,34.98,0.34979999999999994,19496448,48.07954545454545,84.16487358795051,adc_episode_3435,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3436,37.78,0.3778,19496448,48.07954545454545,84.16487358795051,adc_episode_3436,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3437,48.150000000000006,0.48150000000000004,19496448,48.07954545454545,84.16487358795051,adc_episode_3437,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3438,33.120000000000005,0.33120000000000005,19496448,48.07954545454545,84.16487358795051,adc_episode_3438,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3439,42.31,0.42310000000000003,19496448,48.07954545454545,84.16487358795051,adc_episode_3439,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3440,41.059999999999995,0.41059999999999997,19496448,48.07954545454545,84.16487358795051,adc_episode_3440,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3441,42.16,0.4216,19496448,48.07954545454545,84.16487358795051,adc_episode_3441,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3442,38.61,0.3861,19496448,48.07954545454545,84.16487358795051,adc_episode_3442,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3443,34.8,0.348,19496448,48.07954545454545,84.16487358795051,adc_episode_3443,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3444,37.03999999999999,0.3703999999999999,19496448,48.07954545454545,84.16487358795051,adc_episode_3444,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3445,44.620000000000005,0.44620000000000004,19496448,48.07954545454545,84.16487358795051,adc_episode_3445,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3446,38.339999999999996,0.38339999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3446,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3447,39.35999999999999,0.39359999999999995,19496448,48.07954545454545,84.16487358795051,adc_episode_3447,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3448,47.760000000000005,0.4776,19496448,48.07954545454545,84.16487358795051,adc_episode_3448,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3449,44.989999999999995,0.44989999999999997,19496448,48.07954545454545,84.16487358795051,adc_episode_3449,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3450,42.21,0.42210000000000003,19496448,48.07954545454545,84.16487358795051,adc_episode_3450,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3451,36.650000000000006,0.36650000000000005,19496448,48.07954545454545,84.16487358795051,adc_episode_3451,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3452,39.410000000000004,0.39410000000000006,19496448,48.07954545454545,84.16487358795051,adc_episode_3452,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3453,39.28,0.39280000000000004,19496448,48.07954545454545,84.16487358795051,adc_episode_3453,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3454,35.99,0.3599,19496448,48.07954545454545,84.16487358795051,adc_episode_3454,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3455,39.629999999999995,0.39629999999999993,19496448,48.07954545454545,84.16487358795051,adc_episode_3455,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3456,42.22,0.42219999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3456,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3457,41.010000000000005,0.4101000000000001,19496448,48.07954545454545,84.16487358795051,adc_episode_3457,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3458,42.269999999999996,0.42269999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3458,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3459,37.88,0.3788,19496448,48.07954545454545,84.16487358795051,adc_episode_3459,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3460,40.980000000000004,0.40980000000000005,19496448,48.07954545454545,84.16487358795051,adc_episode_3460,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3461,43.82,0.4382,19496448,48.07954545454545,84.16487358795051,adc_episode_3461,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3462,36.370000000000005,0.3637,19496448,48.07954545454545,84.16487358795051,adc_episode_3462,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3463,41.82,0.4182,19496448,48.07954545454545,84.16487358795051,adc_episode_3463,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3464,39.910000000000004,0.3991,19496448,48.07954545454545,84.16487358795051,adc_episode_3464,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3465,31.910000000000004,0.31910000000000005,19496448,48.07954545454545,84.16487358795051,adc_episode_3465,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3466,43.60000000000001,0.4360000000000001,19496448,48.07954545454545,84.16487358795051,adc_episode_3466,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3467,43.47,0.4347,19496448,48.07954545454545,84.16487358795051,adc_episode_3467,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3468,36.31999999999999,0.3631999999999999,19496448,48.07954545454545,84.16487358795051,adc_episode_3468,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3470,34.28,0.3428,19496448,48.07954545454545,84.16487358795051,adc_episode_3470,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3471,42.55,0.4255,19496448,48.07954545454545,84.16487358795051,adc_episode_3471,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3472,37.739999999999995,0.37739999999999996,19496448,48.07954545454545,84.16487358795051,adc_episode_3472,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
-3473,38.38,0.38380000000000003,19496448,48.07954545454545,84.16487358795051,adc_episode_3473,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0, 0, 0, 0, 0, 0, 0, 0, 0]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
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-3475,16.939999999999998,0.16939999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3475,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3476,16.369999999999997,0.16369999999999998,2163456,5.3352272727272725,4.427783754706831,adc_episode_3476,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3477,15.610000000000001,0.15610000000000002,2163456,5.3352272727272725,4.427783754706831,adc_episode_3477,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
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-3480,16.459999999999997,0.16459999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3480,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3481,18.910000000000004,0.18910000000000005,2163456,5.3352272727272725,4.427783754706831,adc_episode_3481,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
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-3483,17.779999999999994,0.17779999999999993,2163456,5.3352272727272725,4.427783754706831,adc_episode_3483,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3484,18.110000000000003,0.18110000000000004,2163456,5.3352272727272725,4.427783754706831,adc_episode_3484,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3485,17.479999999999997,0.17479999999999996,2163456,5.3352272727272725,4.427783754706831,adc_episode_3485,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3486,19.479999999999997,0.19479999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3486,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3487,18.520000000000003,0.18520000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3487,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3488,18.679999999999996,0.18679999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3488,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3489,16.569999999999997,0.16569999999999996,2163456,5.3352272727272725,4.427783754706831,adc_episode_3489,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3490,18.06,0.18059999999999998,2163456,5.3352272727272725,4.427783754706831,adc_episode_3490,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3491,18.53,0.18530000000000002,2163456,5.3352272727272725,4.427783754706831,adc_episode_3491,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3492,18.169999999999998,0.18169999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3492,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
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-3495,18.569999999999997,0.18569999999999998,2163456,5.3352272727272725,4.427783754706831,adc_episode_3495,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3496,18.410000000000004,0.18410000000000004,2163456,5.3352272727272725,4.427783754706831,adc_episode_3496,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3497,17.600000000000005,0.17600000000000005,2163456,5.3352272727272725,4.427783754706831,adc_episode_3497,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3498,17.869999999999997,0.17869999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3498,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3499,20.530000000000005,0.20530000000000004,2163456,5.3352272727272725,4.427783754706831,adc_episode_3499,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3500,21.08,0.2108,2163456,5.3352272727272725,4.427783754706831,adc_episode_3500,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3501,21.4,0.214,2163456,5.3352272727272725,4.427783754706831,adc_episode_3501,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3502,19.389999999999997,0.19389999999999996,2163456,5.3352272727272725,4.427783754706831,adc_episode_3502,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3503,16.490000000000006,0.16490000000000005,2163456,5.3352272727272725,4.427783754706831,adc_episode_3503,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3504,21.47,0.2147,2163456,5.3352272727272725,4.427783754706831,adc_episode_3504,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3505,17.390000000000004,0.17390000000000005,2163456,5.3352272727272725,4.427783754706831,adc_episode_3505,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
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-3507,18.710000000000004,0.18710000000000004,2163456,5.3352272727272725,4.427783754706831,adc_episode_3507,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3508,18.069999999999997,0.18069999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3508,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3509,15.000000000000002,0.15000000000000002,2163456,5.3352272727272725,4.427783754706831,adc_episode_3509,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3510,19.630000000000003,0.19630000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3510,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3511,16.190000000000005,0.16190000000000004,2163456,5.3352272727272725,4.427783754706831,adc_episode_3511,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3512,15.949999999999998,0.15949999999999998,2163456,5.3352272727272725,4.427783754706831,adc_episode_3512,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3513,19.88,0.19879999999999998,2163456,5.3352272727272725,4.427783754706831,adc_episode_3513,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3514,17.579999999999995,0.17579999999999996,2163456,5.3352272727272725,4.427783754706831,adc_episode_3514,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3515,19.479999999999997,0.19479999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3515,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3516,17.410000000000004,0.17410000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3516,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3517,16.679999999999996,0.16679999999999995,2163456,5.3352272727272725,4.427783754706831,adc_episode_3517,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3518,18.320000000000004,0.18320000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3518,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3519,19.069999999999997,0.19069999999999998,2163456,5.3352272727272725,4.427783754706831,adc_episode_3519,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3520,18.369999999999997,0.18369999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3520,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3521,18.14,0.1814,2163456,5.3352272727272725,4.427783754706831,adc_episode_3521,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3522,16.910000000000004,0.16910000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3522,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3523,17.159999999999997,0.17159999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3523,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3524,18.269999999999996,0.18269999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3524,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3525,16.64,0.1664,2163456,5.3352272727272725,4.427783754706831,adc_episode_3525,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
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-3527,16.879999999999995,0.16879999999999995,2163456,5.3352272727272725,4.427783754706831,adc_episode_3527,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3528,17.75,0.1775,2163456,5.3352272727272725,4.427783754706831,adc_episode_3528,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3529,18.069999999999997,0.18069999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3529,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3530,17.769999999999996,0.17769999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3530,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3531,19.55,0.1955,2163456,5.3352272727272725,4.427783754706831,adc_episode_3531,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3532,15.759999999999996,0.15759999999999996,2163456,5.3352272727272725,4.427783754706831,adc_episode_3532,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3533,18.310000000000002,0.1831,2163456,5.3352272727272725,4.427783754706831,adc_episode_3533,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3534,18.42,0.18420000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3534,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3535,18.620000000000005,0.18620000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3535,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3536,17.410000000000004,0.17410000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3536,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3537,18.47,0.18469999999999998,2163456,5.3352272727272725,4.427783754706831,adc_episode_3537,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3538,17.400000000000006,0.17400000000000004,2163456,5.3352272727272725,4.427783754706831,adc_episode_3538,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3539,19.430000000000003,0.19430000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3539,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3540,15.959999999999997,0.15959999999999996,2163456,5.3352272727272725,4.427783754706831,adc_episode_3540,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3541,17.45,0.1745,2163456,5.3352272727272725,4.427783754706831,adc_episode_3541,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3542,17.01,0.17010000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3542,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3543,17.710000000000004,0.17710000000000004,2163456,5.3352272727272725,4.427783754706831,adc_episode_3543,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3544,17.549999999999997,0.17549999999999996,2163456,5.3352272727272725,4.427783754706831,adc_episode_3544,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3545,17.569999999999997,0.17569999999999997,2163456,5.3352272727272725,4.427783754706831,adc_episode_3545,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3546,18.03,0.18030000000000002,2163456,5.3352272727272725,4.427783754706831,adc_episode_3546,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3547,18.75,0.1875,2163456,5.3352272727272725,4.427783754706831,adc_episode_3547,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3548,19.630000000000003,0.19630000000000003,2163456,5.3352272727272725,4.427783754706831,adc_episode_3548,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
-3549,17.700000000000003,0.17700000000000002,2163456,5.3352272727272725,4.427783754706831,adc_episode_3549,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8]"
diff --git a/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv b/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv
deleted file mode 100644
index 2ec675f..0000000
--- a/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/amc.csv
+++ /dev/null
@@ -1,461 +0,0 @@
-episode,top1,reward,total_macs,normalized_macs,normalized_nnz,ckpt_name,action_history,agent_action_history
-1,20.14,0.2014,11442816,28.218749999999996,31.791285637439483,BEST_adc_episode_001,"[0.0625, 0.3125, 0.625, 0.625, 0.5, 0.625, 0.625, 0.28125, 0.75, 0.5, 0.21875, 0.6875, 0.53125, 0.40625, 0.53125, 0.671875, 0.078125, 0.265625, 0.53125]","[0.08415665434674326, 0.32854435046886493, 0.6486487040484364, 0.6835252042090019, 0.5035110998072743, 0.6786214065610782, 0.6395600698628794, 0.31072300294002164, 0.7622393077752324, 0.5293260226085498, 0.22725667289861273, 0.6924470450664589, 0.5619764974297123, 0.4101065610828509, 0.5372018678371113, 0.6771557308763463, 0.08601219658604706, 0.2775437033509216, 0.5409290478672346]"
-2,28.82,0.2882,13038912,32.15482954545455,26.744889725658954,BEST_adc_episode_002,"[0, 0.125, 0.6875, 0.0625, 0.375, 0.375, 0.75, 0.75, 0.6875, 0.21875, 0.59375, 0.5, 0.09375, 0.21875, 0.59375, 0.359375, 0.734375, 0.5, 0.59375]","[0.05360804268833532, 0.14181237259966153, 0.7278991347318444, 0.09356296435859673, 0.3957835611951048, 0.39954152799435555, 0.787769750274963, 0.7574218677226644, 0.6937540595317695, 0.2374685337175311, 0.610984635360392, 0.5134709700284525, 0.11233981965836223, 0.22809205787689196, 0.5978625680997257, 0.3746553335707539, 0.7444932227336121, 0.5037383636004261, 0.6005507504994861]"
-3,29.359999999999996,0.29359999999999997,15170688,37.41193181818182,25.73292092522862,BEST_adc_episode_003,"[0.5, 0.625, 0.125, 0.125, 0, 0.4375, 0.5625, 0.15625, 0.59375, 0.15625, 0.1875, 0.53125, 0.3125, 0.28125, 0.78125, 0.546875, 0.671875, 0.421875, 0.53125]","[0.5381926408115661, 0.6576439712791253, 0.1594183784023404, 0.1566505888983154, 0.056624543937550356, 0.4886464993428731, 0.6047080350911427, 0.1749416695961294, 0.6075143406680599, 0.1701292726155909, 0.19794974168801277, 0.5619313945122496, 0.3238983839963902, 0.2962214575559744, 0.7819758790574471, 0.561677844864734, 0.6870289297090059, 0.42596927219357417, 0.5444535649359525]"
-4,55.879999999999995,0.5588,17638848,43.49857954545455,44.54679935449166,BEST_adc_episode_004,"[0.25, 0.5, 0.1875, 0.6875, 0.0625, 0.25, 0.375, 0.21875, 0.15625, 0.5, 0.40625, 0.21875, 0.65625, 0.375, 0.15625, 0.359375, 0.140625, 0.46875, 0.4375]","[0.25617259183747465, 0.5216085684883116, 0.19943561220590345, 0.7269383140335177, 0.10979604175666328, 0.2608511324810973, 0.41704316868345753, 0.22164703329538896, 0.1767628179191009, 0.5183820296835726, 0.43626509980289, 0.22292158485735383, 0.6679594185249424, 0.3884022075500952, 0.16165207142709367, 0.37496217093260314, 0.15047864042685724, 0.470018561530088, 0.4475801007040172]"
-5,25.8,0.258,15553152,38.35511363636363,49.66043571812803,BEST_adc_episode_005,"[0.6875, 0.5, 0.25, 0.6875, 0, 0.75, 0.6875, 0.28125, 0.34375, 0.125, 0.625, 0.46875, 0.125, 0.09375, 0.375, 0.4375, 0.125, 0.203125, 0.40625]","[0.7203147610238094, 0.5046818526535961, 0.2777089053656343, 0.7168948388012164, 0.05502895989677375, 0.7842806366180027, 0.7030125539483185, 0.28430148527063936, 0.35285090399254926, 0.13934839900001156, 0.6479856720635855, 0.49893883706102726, 0.14964412129223262, 0.09605846583223457, 0.38320693148458645, 0.4518891912354004, 0.13974595092167386, 0.20591248980000146, 0.41750003491739124]"
-6,21.030000000000005,0.21030000000000004,12989952,32.03409090909091,35.718128025820334,BEST_adc_episode_006,"[0.3125, 0.6875, 0.75, 0.625, 0.25, 0.625, 0.0625, 0.28125, 0.75, 0.4375, 0.125, 0.46875, 0.09375, 0.734375, 0.5625, 0.0625, 0.203125, 0.578125, 0.40625]","[0.3651333703312157, 0.7494490090203013, 0.7514260572864256, 0.6384462970857672, 0.2791755661739046, 0.6857471815758827, 0.11737416436758677, 0.2959293147065506, 0.7661026570986929, 0.44438667503728607, 0.1509835162358225, 0.48108278977189844, 0.11725019642698324, 0.7496367884790699, 0.5659241396406816, 0.06611302926191445, 0.21670274929224856, 0.5837547311016283, 0.41718882716798494]"
-7,22.37,0.2237,12310272,30.357954545454547,35.65424959655729,BEST_adc_episode_007,"[0.4375, 0.1875, 0.75, 0.4375, 0.75, 0.5, 0.4375, 0.6875, 0.59375, 0.71875, 0.25, 0.25, 0.21875, 0.109375, 0.796875, 0.375, 0.5625, 0.09375, 0.171875]","[0.4909749135819981, 0.23115585333970692, 0.7547475150248849, 0.4603544520282301, 0.7671774387048116, 0.534768662737602, 0.4639399633242757, 0.715990433150231, 0.5939026390799211, 0.7201508642602125, 0.26027878363909784, 0.25612938956034537, 0.23265509942478957, 0.11474409025090215, 0.7978890596227087, 0.38040061914337686, 0.5726004342013682, 0.10062824289114514, 0.18370957235228585]"
-8,38.349999999999994,0.38349999999999995,19117440,47.14488636363636,38.8145508337816,BEST_adc_episode_008,"[0.0625, 0.25, 0.3125, 0.125, 0.0625, 0.375, 0.5625, 0.09375, 0.34375, 0.0625, 0.78125, 0.1875, 0.1875, 0.109375, 0.359375, 0.25, 0.703125, 0.515625, 0.59375]","[0.09234433118977872, 0.25700258885336735, 0.314892288861078, 0.16363245351693717, 0.06855952239419505, 0.3848359727633564, 0.5789473643965402, 0.12207874621650142, 0.36416932070465907, 0.06649605206172737, 0.7987095809459409, 0.21298780195190736, 0.19842954751922584, 0.11360339066293053, 0.3639787582602525, 0.25431893928067806, 0.7081524872228547, 0.5250076521331556, 0.6070292608164184]"
-9,22.550000000000004,0.22550000000000003,13127616,32.37357954545455,28.039268423883808,BEST_adc_episode_009,"[0.625, 0.125, 0.4375, 0.5625, 0.625, 0.125, 0.6875, 0.71875, 0.03125, 0.1875, 0.5, 0.40625, 0.125, 0.59375, 0.640625, 0.59375, 0.703125, 0.328125, 0.1875]","[0.6787675690868734, 0.16360282495872172, 0.44635884881373716, 0.6050771189558006, 0.6828425586820386, 0.15413113459883074, 0.72076820389247, 0.742637744635814, 0.06126067437035551, 0.19083893798371515, 0.5042257758422262, 0.4157039211931743, 0.1325647427480403, 0.5971476755190213, 0.6490887261934016, 0.6013418212700179, 0.7087828162787251, 0.3346820747001173, 0.1928331381803466]"
-10,12.709999999999999,0.1271,11030400,27.201704545454547,33.2672135556751,BEST_adc_episode_010,"[0.6875, 0.6875, 0.6875, 0.0625, 0.6875, 0.3125, 0.6875, 0.25, 0.09375, 0.78125, 0.25, 0.78125, 0.53125, 0.21875, 0.171875, 0.734375, 0.328125, 0.40625, 0.40625]","[0.7128625602492684, 0.6888777735617378, 0.7382022551873767, 0.06464080165495548, 0.7215197418220269, 0.3584504730571794, 0.6937918522164219, 0.2721984329456287, 0.11583136729621372, 0.7927795020196845, 0.2715256972597982, 0.7818259824097007, 0.5467449980383966, 0.23293919286650736, 0.1736113873941229, 0.7472918689548773, 0.33290360857304374, 0.41896417419118, 0.42005366822082363]"
-11,16.45,0.16449999999999998,11627136,28.673295454545457,20.854626143087682,BEST_adc_episode_011,"[0.4375, 0.375, 0.5625, 0.125, 0.0625, 0.3125, 0.6875, 0.71875, 0.3125, 0.625, 0.625, 0.53125, 0.46875, 0.453125, 0.625, 0.484375, 0.703125, 0.4375, 0.625]","[0.43775103957965333, 0.40740289016737297, 0.5925167919701136, 0.13186235910265093, 0.09615350376614676, 0.32015082695814207, 0.6912478731800327, 0.7252226603201957, 0.3412566773590955, 0.6560286651689949, 0.6477407447862836, 0.5363761065270825, 0.49169493198945763, 0.45605049634897576, 0.6384510122077782, 0.4913173918481692, 0.7140947105901895, 0.4435123079202311, 0.6366553952497497]"
-12,38.660000000000004,0.38660000000000005,14639040,36.10085227272727,44.48628294782141,BEST_adc_episode_012,"[0.3125, 0.625, 0.625, 0.4375, 0.5625, 0.1875, 0.5, 0.3125, 0.75, 0.5, 0.21875, 0.3125, 0.4375, 0.328125, 0.15625, 0.171875, 0.15625, 0.640625, 0.390625]","[0.36868569715808946, 0.6391646237212247, 0.6400291332923503, 0.4591450008502731, 0.5855329861184739, 0.24788565855670083, 0.5389015321608888, 0.3150195735846907, 0.753403861500634, 0.5257582068669044, 0.23626338310717915, 0.3140199974330242, 0.45733874788040657, 0.3302255730931243, 0.16896275487296575, 0.18456395238338075, 0.16053537841260135, 0.6414069023607339, 0.40105385881339334]"
-13,40.339999999999996,0.4034,15265728,37.64630681818182,40.7914201183432,BEST_adc_episode_013,"[0.3125, 0.6875, 0.1875, 0.6875, 0.0625, 0.3125, 0.5625, 0.15625, 0.09375, 0.65625, 0.40625, 0.53125, 0.3125, 0.078125, 0.078125, 0.671875, 0.453125, 0.390625, 0.453125]","[0.3485265485921212, 0.694113118274569, 0.20208981036887486, 0.74030133621399, 0.1200334825130983, 0.3205918309999974, 0.5818519809217233, 0.1570712606433585, 0.09951516962635036, 0.6704353095835456, 0.4279687436305971, 0.5523364260191368, 0.34114703250188916, 0.0885748026934649, 0.0925954521160026, 0.6794618280801918, 0.4583095886068996, 0.40034455795772, 0.45567575263371557]"
-14,41.120000000000005,0.41120000000000007,19315008,47.63210227272727,40.41151156535772,BEST_adc_episode_014,"[0, 0.125, 0.4375, 0.4375, 0.125, 0, 0.1875, 0.46875, 0.625, 0.34375, 0.21875, 0.5, 0.25, 0.234375, 0.390625, 0.625, 0.25, 0.390625, 0.21875]","[0.05649311393123628, 0.161069207698615, 0.4463642571796586, 0.45544644547054564, 0.183194450003662, 0.056868663680079375, 0.20857070527782154, 0.4688894369617454, 0.6432603267590721, 0.34444474907500566, 0.22709213303070186, 0.5067353901337112, 0.2671983300265322, 0.24887153194028025, 0.40496998690730684, 0.6350328970384083, 0.2651592378364056, 0.3996641067129697, 0.22133204453221872]"
-15,24.31,0.24309999999999998,12528576,30.896306818181817,30.62466379774072,BEST_adc_episode_015,"[0.375, 0.0625, 0.6875, 0.625, 0.0625, 0.5, 0.375, 0.09375, 0.5, 0.625, 0.75, 0.65625, 0.40625, 0.15625, 0.453125, 0.65625, 0.3125, 0.484375, 0.484375]","[0.4127720741135361, 0.08126981196461153, 0.7312762188578077, 0.6387742384635583, 0.07576080920194199, 0.5330892524627092, 0.3789180933246046, 0.10632139518034572, 0.5177461616178789, 0.6498328767323167, 0.7558869402280746, 0.6668990653165101, 0.4374086668189861, 0.16944099888875386, 0.46283634557309566, 0.66875052455862, 0.3254492853572277, 0.48919535649081847, 0.49771973381135654]"
-16,22.629999999999995,0.22629999999999995,11501568,28.363636363636363,28.472969338353955,BEST_adc_episode_016,"[0.75, 0.5, 0.75, 0.5, 0.375, 0.125, 0.75, 0.46875, 0.5625, 0.59375, 0.21875, 0.21875, 0.40625, 0.421875, 0.390625, 0.453125, 0.5, 0.640625, 0.453125]","[0.7860990025889187, 0.5214392259694545, 0.7753291140770996, 0.540801036120431, 0.3784658119762309, 0.16253434829724775, 0.7747328417577131, 0.47776549978192484, 0.5637655685687791, 0.6234706873357188, 0.2190615270999349, 0.23370692824428338, 0.40861192824457043, 0.42538588925809667, 0.3986240828270672, 0.46127871033410317, 0.5011649805417244, 0.6485253611099112, 0.4598678154451124]"
-17,29.990000000000006,0.29990000000000006,15580800,38.42329545454545,46.45306616460463,BEST_adc_episode_017,"[0.25, 0.125, 0.625, 0.75, 0.625, 0.3125, 0.1875, 0.1875, 0.46875, 0.625, 0.59375, 0.59375, 0.09375, 0.1875, 0.1875, 0.09375, 0.265625, 0.734375, 0.234375]","[0.2500205105647346, 0.16343295834249716, 0.6395078153378355, 0.7591319039675767, 0.6785520067276138, 0.32146135607050336, 0.22902823706134123, 0.21464007150669048, 0.47272209792689696, 0.6303723241748297, 0.597423148542652, 0.5966668293617569, 0.12094705732200396, 0.20135361834671625, 0.19284113655473736, 0.09375788205339483, 0.2694273598268846, 0.7423024639473168, 0.24707037115258437]"
-18,12.509999999999998,0.1251,11657088,28.74715909090909,29.431145777299623,BEST_adc_episode_018,"[0.0625, 0.1875, 0.375, 0.5625, 0.375, 0.6875, 0.6875, 0.75, 0.5, 0.5625, 0.75, 0.34375, 0.65625, 0.765625, 0.765625, 0.0625, 0.0625, 0.578125, 0.5625]","[0.11413571530873237, 0.19924696184608254, 0.39786235376684054, 0.6043905046814614, 0.41817763158444826, 0.7377984350225674, 0.7170142733750019, 0.7648826239431596, 0.5178777620471352, 0.5632759677638715, 0.7776489813914367, 0.3641000299910561, 0.6657795661238449, 0.7766346101817851, 0.7771603422854785, 0.07522803538098446, 0.07550036755156478, 0.5864374628573674, 0.5754639274018913]"
-19,46.34,0.46340000000000003,14806656,36.51420454545454,33.41178052716514,BEST_adc_episode_019,"[0.1875, 0.125, 0.1875, 0.25, 0.625, 0.5625, 0.1875, 0.5, 0.46875, 0.5, 0.4375, 0.65625, 0.4375, 0.3125, 0.46875, 0.3125, 0.34375, 0.578125, 0.4375]","[0.22083903312535333, 0.15907154285870206, 0.23716853279113756, 0.28820357668330754, 0.6450370432237814, 0.6153392223480605, 0.21617561390705975, 0.5106020060480139, 0.4984991949112175, 0.5178734763392482, 0.45123792583606126, 0.6709553365286474, 0.44173590182085076, 0.3225667365367055, 0.4829965596156546, 0.32636600052685993, 0.3565354669989418, 0.5822947376568358, 0.4389928517556827]"
-20,16.679999999999996,0.16679999999999995,9063936,22.352272727272727,28.234265734265733,BEST_adc_episode_020,"[0.6875, 0.625, 0.4375, 0.4375, 0.625, 0.625, 0.3125, 0.28125, 0.71875, 0.78125, 0.71875, 0.6875, 0.71875, 0.578125, 0.171875, 0.546875, 0.609375, 0.34375, 0.140625]","[0.7286645126555898, 0.6834212687551426, 0.45724525394832805, 0.49565649614245066, 0.6634853006083051, 0.646866981311676, 0.3695953900906111, 0.3038235983116045, 0.7224876983339622, 0.7825049424008171, 0.7366273020706814, 0.6959726874896738, 0.7286278822888193, 0.587825930057341, 0.1771894692944859, 0.5489241086226175, 0.6100560319170956, 0.3473441803261714, 0.1450269979050301]"
-21,43.96,0.4396,12966336,31.975852272727273,41.42011834319527,BEST_adc_episode_021,"[0.5, 0.625, 0.5, 0.3125, 0.75, 0.5, 0.5, 0.78125, 0.46875, 0.625, 0.3125, 0.1875, 0.46875, 0.171875, 0.21875, 0.109375, 0.28125, 0.703125, 0.65625]","[0.5035013303440234, 0.6434604572303826, 0.5168572137966572, 0.3697686280999205, 0.7505280388302401, 0.5238981908403633, 0.5399247162927556, 0.7982684299268645, 0.4748798813609883, 0.6331586822689026, 0.32212716856510376, 0.19785620034716964, 0.4856425604214159, 0.1806062764797957, 0.22994310552899855, 0.12079285333715317, 0.2961650796691456, 0.7047827171373259, 0.6668857075037727]"
-22,32.31,0.3231,14399424,35.50994318181818,33.929532006455084,BEST_adc_episode_022,"[0.625, 0.3125, 0.0625, 0.1875, 0.75, 0.4375, 0.4375, 0.28125, 0.71875, 0.75, 0.1875, 0.3125, 0.09375, 0.296875, 0.59375, 0.5625, 0.34375, 0.265625, 0.59375]","[0.6871352938442369, 0.3307765947713608, 0.10394221968535022, 0.21877512405351127, 0.7735411413112073, 0.4895500620005025, 0.484642677972515, 0.28537380949989444, 0.726738518288259, 0.7524119838782954, 0.19710271296828513, 0.33833774485836016, 0.09694521450107266, 0.29829923146578136, 0.6034138643379657, 0.5726224224505304, 0.34478914799519234, 0.2694937984424399, 0.5979031386183021]"
-23,35.540000000000006,0.35540000000000005,14073984,34.70738636363636,39.27178590640129,BEST_adc_episode_023,"[0.125, 0.375, 0.625, 0.25, 0.3125, 0.6875, 0.0625, 0.40625, 0.3125, 0.5625, 0.6875, 0.65625, 0.6875, 0.25, 0.546875, 0.09375, 0.328125, 0.40625, 0.15625]","[0.15474534510156346, 0.43706323942496217, 0.6782911577173081, 0.2720501738825616, 0.3168168721143689, 0.6883228044132428, 0.0636033535850188, 0.41534969581785386, 0.33700114123686664, 0.5702809880851735, 0.7012314406500562, 0.6658355787269398, 0.6895494706552794, 0.2508389981606529, 0.5615484684186381, 0.10490548348862488, 0.329309283371062, 0.4134190630777204, 0.15976283040309586]"
-24,26.029999999999998,0.2603,15381504,37.93181818181818,39.591178052716515,BEST_adc_episode_024,"[0.5, 0.5625, 0.625, 0.1875, 0.4375, 0.3125, 0.5, 0.09375, 0.625, 0.0625, 0.09375, 0.46875, 0.71875, 0.28125, 0.75, 0.734375, 0.203125, 0.046875, 0.125]","[0.5197173027390133, 0.5920540741990695, 0.6336557414886463, 0.1996774915472747, 0.44109430666175975, 0.36003168109960065, 0.5396809910344693, 0.09555601717368806, 0.637013542496133, 0.09094247392717182, 0.11832679298057398, 0.470993083030376, 0.7410012120025692, 0.2854372371158394, 0.7535836363274591, 0.7492776788108317, 0.21725672751007485, 0.055175325912780446, 0.13845073970968472]"
-25,40.92,0.4092,16923456,41.734375,38.777568585260894,BEST_adc_episode_025,"[0.75, 0.1875, 0.5, 0, 0.3125, 0.3125, 0.125, 0.3125, 0.4375, 0.5, 0.59375, 0.1875, 0.1875, 0.046875, 0.765625, 0.140625, 0.296875, 0.5625, 0.15625]","[0.7581737006137664, 0.2419266801228323, 0.5622454228253139, 0.05370712429621415, 0.3327837255914403, 0.3598361870582173, 0.12916529737997812, 0.31913075882720127, 0.4399799006824089, 0.5204819556361114, 0.6130724428250659, 0.18853422743251305, 0.2061687064234162, 0.057389510937001675, 0.780307166475636, 0.1414783658121581, 0.2974855056993596, 0.5630332625220957, 0.16955239990533721]"
-26,30.94,0.3094,14488128,35.72869318181818,37.311726734803656,BEST_adc_episode_026,"[0.6875, 0.25, 0.25, 0.5625, 0.5625, 0.5, 0.0625, 0.25, 0.6875, 0.375, 0.5, 0.21875, 0.34375, 0.140625, 0.59375, 0.4375, 0.3125, 0.296875, 0.5]","[0.7456347982525983, 0.3114646101767517, 0.26336197629319935, 0.6093973858418028, 0.6067916775668762, 0.5047389050194847, 0.10077763723270698, 0.2546151112832366, 0.7010756540119522, 0.39615320773996443, 0.5199864549573392, 0.22261315172532856, 0.3539892832895928, 0.15217249488472956, 0.6056111391346916, 0.44169800719321206, 0.31436647969644665, 0.30437972496838717, 0.5000067886249961]"
-27,27.24,0.2724,11443392,28.220170454545457,20.488165680473372,BEST_adc_episode_027,"[0.1875, 0.375, 0.6875, 0.5, 0.1875, 0.5, 0.375, 0.46875, 0.03125, 0.59375, 0.625, 0.25, 0.59375, 0.4375, 0.59375, 0.46875, 0.734375, 0.609375, 0.75]","[0.22286262293948422, 0.41761547935796406, 0.7247749763775426, 0.5193766020794508, 0.1898145021408576, 0.5602538817352112, 0.38965493119585853, 0.4728583615212735, 0.05908629363157567, 0.6170423947631406, 0.634761257325408, 0.2510359527516137, 0.6138749534155998, 0.43964065234720057, 0.6025285041456259, 0.48163578423653136, 0.738799105752362, 0.6173533853525461, 0.7577397023639314]"
-28,13.690000000000001,0.13690000000000002,11242368,27.724431818181817,34.05056481979559,BEST_adc_episode_028,"[0.125, 0.4375, 0.6875, 0.625, 0.5625, 0.5625, 0.3125, 0.65625, 0.59375, 0.375, 0.6875, 0.59375, 0.46875, 0.0625, 0.515625, 0.59375, 0.203125, 0.484375, 0.078125]","[0.14990413042176598, 0.499567678039238, 0.7459095696812721, 0.6522803294391476, 0.6006581191613676, 0.5783612061153424, 0.3728105902291372, 0.6587408033663805, 0.5968210400037818, 0.39175785289136855, 0.7028415980082336, 0.6232167036201832, 0.4719762168333647, 0.07598088675717063, 0.5287599733438473, 0.5992850458350666, 0.21507891139233215, 0.48737507178340334, 0.07818367928090472]"
-29,38.13999999999999,0.38139999999999996,15562368,38.37784090909091,36.60233996772458,BEST_adc_episode_029,"[0.5625, 0.5625, 0.1875, 0.5, 0.1875, 0.5625, 0.5625, 0.46875, 0.03125, 0.0625, 0.53125, 0.1875, 0.5, 0.21875, 0.421875, 0.609375, 0.078125, 0.5, 0.703125]","[0.5689930732658992, 0.5819183562276765, 0.21022197615497543, 0.5122822725812615, 0.22890890046450693, 0.5887161466397791, 0.5867695143883134, 0.4773591867251582, 0.059972721050101555, 0.07036262114007659, 0.5607243667702724, 0.18970135742324673, 0.5220669278808754, 0.22719008825243103, 0.43476204076864405, 0.6149312066058823, 0.08104227545028161, 0.5049297806228846, 0.705415623853934]"
-30,18.42,0.18420000000000003,13746816,33.90056818181818,46.268154922001074,BEST_adc_episode_030,"[0.5625, 0.6875, 0.5625, 0.375, 0.75, 0.6875, 0.4375, 0.5, 0.09375, 0.21875, 0.59375, 0.6875, 0.21875, 0.15625, 0.046875, 0.40625, 0.5625, 0.3125, 0.21875]","[0.577496871087258, 0.7054690709875491, 0.6082647493060366, 0.425733477954382, 0.7997523516442773, 0.6957049699319452, 0.4516097498064566, 0.509549336053908, 0.11183761661059077, 0.2220763145807243, 0.6224310490498709, 0.7077635609363195, 0.23286985998924525, 0.15675722433004072, 0.06199648397219792, 0.4092154289134458, 0.5653023408769873, 0.3176063824751568, 0.22520032751770697]"
-31,42.66,0.4266,16641792,41.03977272727273,39.79626143087682,BEST_adc_episode_031,"[0.3125, 0.75, 0.5, 0.5625, 0.125, 0.25, 0.1875, 0.25, 0.125, 0.59375, 0.1875, 0.40625, 0.0625, 0.40625, 0.6875, 0.53125, 0.3125, 0.1875, 0.28125]","[0.36286229179359464, 0.7910517424596337, 0.5439680150491928, 0.592590603754052, 0.15107580021417488, 0.29914736126895053, 0.22828329578121836, 0.2716009391093814, 0.12607703746942056, 0.6103086530216374, 0.19611498115655282, 0.4158203810130716, 0.08563285243660865, 0.417467996716756, 0.6933588160287073, 0.542829577484465, 0.31841821388354724, 0.19670082909631625, 0.28438929848782346]"
-32,40.5,0.405,15737472,38.80965909090909,42.57665411511565,BEST_adc_episode_032,"[0.3125, 0.375, 0.5625, 0.375, 0.1875, 0.5, 0.5, 0.03125, 0.75, 0.71875, 0.34375, 0.25, 0.0625, 0.484375, 0.0625, 0.1875, 0.703125, 0.15625, 0.421875]","[0.3569067049635647, 0.4046485295781274, 0.5628904258140921, 0.39017496729107887, 0.20964497082060174, 0.52749513790483, 0.5575161586035997, 0.05910935111679758, 0.7641634723157937, 0.721599796798192, 0.3459028840261735, 0.2648666914798087, 0.07606002040546365, 0.4846949969335701, 0.06889456178882669, 0.20243989237310325, 0.7181120121334039, 0.16450340518166923, 0.43518996105858554]"
-33,21.950000000000003,0.21950000000000003,12859200,31.71164772727273,33.02850995158688,BEST_adc_episode_033,"[0.5625, 0.125, 0.5, 0.375, 0.6875, 0.4375, 0.1875, 0.28125, 0.53125, 0.75, 0.59375, 0.4375, 0.21875, 0.5625, 0.546875, 0.671875, 0.0625, 0.34375, 0.3125]","[0.5935383698294882, 0.16538999142760485, 0.5051935691333265, 0.43705103031814235, 0.732002320975447, 0.45383716302570115, 0.1967880666195715, 0.3092186496348858, 0.5609431075160898, 0.7523503317075753, 0.60473519400266, 0.4447579970752885, 0.24251586274712508, 0.5754964192162746, 0.5493849893006277, 0.6756272710954215, 0.06365652652169941, 0.3531485343723436, 0.31622719379188113]"
-34,43.2,0.43200000000000005,14390784,35.48863636363636,34.19176976869285,BEST_adc_episode_034,"[0.125, 0.5625, 0.4375, 0.375, 0.1875, 0.375, 0.6875, 0.15625, 0.5, 0.5, 0.46875, 0.4375, 0.09375, 0.453125, 0.328125, 0.359375, 0.359375, 0.5625, 0.703125]","[0.16212321001086966, 0.5683689674104652, 0.4659216194437245, 0.396310595516552, 0.1931125723052728, 0.3839913586983972, 0.7176058627847585, 0.17133945643822013, 0.5196539635266146, 0.5254797231841974, 0.4838595261528536, 0.4604499871768091, 0.111646490869276, 0.45652587033280073, 0.33884327250567636, 0.36396294705820403, 0.37165365296584835, 0.5682391827304203, 0.7032836349450463]"
-35,44.769999999999996,0.4477,15801984,38.96875,41.036847767617,BEST_adc_episode_035,"[0.6875, 0.1875, 0.5, 0.5, 0.4375, 0.0625, 0.0625, 0.75, 0.4375, 0.1875, 0.6875, 0.4375, 0.125, 0.203125, 0.328125, 0.5625, 0.140625, 0.359375, 0.53125]","[0.7412267744545427, 0.19211677660408594, 0.5131563695814901, 0.5394942946315308, 0.48532643718966884, 0.08845383624478413, 0.07419064522907301, 0.7533776577522653, 0.4518489452101756, 0.18796037467763102, 0.7140290947793512, 0.4423639097599487, 0.12876652578020253, 0.21013122158968206, 0.3331504358519686, 0.5699930109576302, 0.14621800630422738, 0.3732033085401438, 0.5319914909659126]"
-36,37.46000000000001,0.3746000000000001,13415040,33.08238636363636,31.814819795589028,BEST_adc_episode_036,"[0.3125, 0.5625, 0.5, 0.1875, 0.4375, 0.25, 0.1875, 0.5, 0.40625, 0.5, 0.59375, 0.6875, 0.375, 0.5625, 0.1875, 0.53125, 0.453125, 0.25, 0.625]","[0.3447101505012113, 0.5838981924127399, 0.5403933631040821, 0.23443319690356285, 0.4812517587557209, 0.26331232070782834, 0.24716079392835416, 0.5082449002671847, 0.41143814886226215, 0.5204242264749827, 0.6064286264503532, 0.6997223462748335, 0.37530152055667443, 0.5778209548922094, 0.20239726472971417, 0.5411581234954986, 0.46830891555270204, 0.2617165957654559, 0.6308999571064315]"
-37,15.43,0.1543,11098944,27.370738636363633,20.141877353415815,BEST_adc_episode_037,"[0.25, 0.75, 0.6875, 0.125, 0.5, 0.375, 0.4375, 0.21875, 0.40625, 0.28125, 0.65625, 0.15625, 0.46875, 0.671875, 0.203125, 0.796875, 0.71875, 0.5625, 0.6875]","[0.2801990872614437, 0.7835054049536629, 0.7049100893075522, 0.17538644244083984, 0.5549783652507075, 0.377571777906766, 0.4748324760903627, 0.22645441506199476, 0.4314956181867264, 0.2829067994879273, 0.6581208238666288, 0.18368149776698373, 0.48578113527478184, 0.6840097076411455, 0.20542241548310958, 0.798355373040485, 0.720546468533511, 0.5702787790246696, 0.6913895329976759]"
-38,52.94,0.5294,17091648,42.14914772727273,43.269230769230774,BEST_adc_episode_038,"[0.4375, 0.6875, 0.6875, 0.625, 0.0625, 0.0625, 0.5625, 0.75, 0.125, 0.0625, 0.25, 0.40625, 0.0625, 0.15625, 0.375, 0.09375, 0.53125, 0.484375, 0.703125]","[0.47861893279311746, 0.7316206376803995, 0.6962840921972365, 0.6617968241636097, 0.07258772316023646, 0.0708178214826691, 0.6020492255010722, 0.7733434632108909, 0.15029889592143303, 0.07066261584657235, 0.2562737809282449, 0.4185947969308069, 0.06307934371167966, 0.16279989731830674, 0.3836653765283789, 0.10184011357107492, 0.5389149267999395, 0.49514218456193804, 0.7034525008100617]"
-39,12.250000000000005,0.12250000000000005,9530496,23.502840909090907,28.879774072081766,BEST_adc_episode_039,"[0.4375, 0.375, 0.75, 0.75, 0.125, 0.75, 0.25, 0.65625, 0.71875, 0.78125, 0.15625, 0.71875, 0.09375, 0.78125, 0.421875, 0.65625, 0.53125, 0.125, 0.109375]","[0.4534405261922781, 0.42175674334356467, 0.7544734881186071, 0.7958845661227978, 0.14930969936391475, 0.7771708644295204, 0.2947702245152878, 0.6684316414481555, 0.7303533387074765, 0.78383200482477, 0.1599933787160464, 0.7477042517794104, 0.11959154238334638, 0.7864889508615092, 0.4227206413453241, 0.671157891404743, 0.5423699665983726, 0.1301335734166033, 0.11208440305523618]"
-40,24.039999999999996,0.24039999999999995,16517376,40.73295454545455,53.42253899946208,BEST_adc_episode_040,"[0.0625, 0.5625, 0.375, 0.375, 0.75, 0.625, 0.0625, 0.25, 0.46875, 0.09375, 0.75, 0.34375, 0.65625, 0.296875, 0.046875, 0.234375, 0.21875, 0.109375, 0.453125]","[0.11547207295503396, 0.5974471823709775, 0.43354005177772154, 0.43300953673010045, 0.7791096172670037, 0.6478683585898132, 0.11911053502483382, 0.2771840567357244, 0.49525558388557817, 0.11494579700051342, 0.7560354210495888, 0.35623030160315844, 0.6782798890438955, 0.306722026729264, 0.05545611278639478, 0.23481760108369565, 0.23083438003573445, 0.1123076267747082, 0.45735833035052526]"
-41,27.949999999999996,0.27949999999999997,13521600,33.34517045454545,35.70467993544917,BEST_adc_episode_041,"[0.6875, 0.75, 0.0625, 0.4375, 0.5625, 0.6875, 0.1875, 0.0625, 0.625, 0.6875, 0.09375, 0.09375, 0.65625, 0.765625, 0.09375, 0.625, 0.296875, 0.140625, 0.453125]","[0.6973208994722108, 0.763505154709304, 0.09462902583057611, 0.4530658239141141, 0.5971563907015444, 0.7426782112415958, 0.22234356727229304, 0.07759346573852409, 0.6446135223536624, 0.7060980568371618, 0.0980219802794806, 0.1197037938544425, 0.6802282493019102, 0.7805099341636221, 0.10354874571852654, 0.6289969626806778, 0.31241353960072205, 0.14538916106278565, 0.4602935340445116]"
-42,34.41,0.34409999999999996,17415936,42.94886363636364,50.88084991931146,BEST_adc_episode_042,"[0.25, 0.4375, 0.1875, 0.4375, 0.625, 0.75, 0, 0.65625, 0.125, 0.125, 0.65625, 0.1875, 0.3125, 0.359375, 0.375, 0.0625, 0.265625, 0.265625, 0.265625]","[0.3114606469970353, 0.4611110622010755, 0.22798455225679945, 0.4687952098398968, 0.6596657371937032, 0.7545021695615488, 0.05663260816355842, 0.6734051033621834, 0.144642427255961, 0.13539809095610794, 0.6722586302338927, 0.2053086299026835, 0.33434902519283566, 0.37074514109713697, 0.3816302647130454, 0.0676967007708293, 0.2703833273476013, 0.2743720403369938, 0.28006453785039365]"
-43,19.53,0.1953,9758016,24.063920454545453,25.830419580419576,BEST_adc_episode_043,"[0.625, 0.75, 0.4375, 0.6875, 0.4375, 0.75, 0.5625, 0.65625, 0.0625, 0.5625, 0.40625, 0.46875, 0.0625, 0.578125, 0.28125, 0.625, 0.484375, 0.703125, 0.21875]","[0.6514336795395763, 0.7547832217405284, 0.4519256147995766, 0.6900966595620435, 0.4717545973507047, 0.7994291253613529, 0.5958030221823367, 0.6794159099241839, 0.0817543054727203, 0.5832210593429009, 0.42476001176447786, 0.4955647881005724, 0.08913048472166371, 0.5863984561688284, 0.2933475850423722, 0.6327426338421542, 0.49746245658826765, 0.7037778048150193, 0.22513744218094833]"
-44,11.939999999999994,0.11939999999999994,15576768,38.41335227272727,49.30406132329209,BEST_adc_episode_044,"[0.5625, 0.5625, 0.4375, 0.5625, 0.625, 0.375, 0.0625, 0.09375, 0.65625, 0.15625, 0.53125, 0.6875, 0.125, 0.078125, 0.125, 0.171875, 0.625, 0.109375, 0.515625]","[0.5790369434537883, 0.5848427736412851, 0.473569351849958, 0.6004084083722095, 0.6638009176243744, 0.3808679339921294, 0.07195832914758267, 0.11731314514836196, 0.6749700669318026, 0.15807761708446488, 0.5610675784977913, 0.6994170090528631, 0.13583436963116288, 0.0933700870004391, 0.13551406153941972, 0.17908026557776394, 0.6396529896821271, 0.11625797561826309, 0.523038005509525]"
-45,49.7,0.49700000000000005,17512128,43.18607954545455,37.42267348036579,BEST_adc_episode_045,"[0.3125, 0.0625, 0.1875, 0.125, 0.625, 0.625, 0.5, 0.34375, 0.59375, 0.125, 0.125, 0.375, 0.125, 0.078125, 0.390625, 0.53125, 0.625, 0.4375, 0.578125]","[0.31962933886096967, 0.11523949860153185, 0.22022608536654684, 0.16493643558217058, 0.672376043337358, 0.6413728853436598, 0.5381027472255853, 0.3534091933819736, 0.6080793456255168, 0.12872869382481905, 0.13523050929481453, 0.3830035093148902, 0.12863809378250243, 0.08614900280577976, 0.40559817507954193, 0.5380602729135602, 0.6342951443268166, 0.44646720724796896, 0.585232310890568]"
-46,27.139999999999997,0.2714,15800832,38.96590909090909,39.46678321678322,BEST_adc_episode_046,"[0.0625, 0.3125, 0.375, 0.125, 0.5625, 0.5625, 0.3125, 0.34375, 0.375, 0.46875, 0.125, 0.75, 0.53125, 0.4375, 0.609375, 0.046875, 0.203125, 0.4375, 0.421875]","[0.06466627388561792, 0.32155738126707956, 0.4084001995007673, 0.14296044234082833, 0.6184694436129938, 0.5826349715635979, 0.3728045913492934, 0.36867404145266075, 0.40531753888012073, 0.4732237414929397, 0.15224264489575842, 0.7557420097908895, 0.5590644628838901, 0.4509180669844561, 0.6219183418260227, 0.05612395202057969, 0.20702532789894335, 0.4397423215923577, 0.4312607028436598]"
-47,29.720000000000002,0.2972,15034176,37.075284090909086,28.79908552985476,BEST_adc_episode_047,"[0.125, 0.4375, 0, 0.625, 0.125, 0.4375, 0.625, 0.4375, 0.09375, 0.0625, 0.375, 0.65625, 0.53125, 0.25, 0.484375, 0.59375, 0.34375, 0.765625, 0.453125]","[0.1565153433238336, 0.48076392892024417, 0.053331116663652124, 0.6680664359193282, 0.16058439526517987, 0.46744615800320655, 0.6483675127199904, 0.44536018617746326, 0.11316278877271191, 0.06657103416762147, 0.38214582193909846, 0.6569535657368574, 0.5480321313856822, 0.26135323236400376, 0.48700923732182705, 0.5977369673384312, 0.3586666747038097, 0.7754436620109869, 0.4683895723524275]"
-48,11.2,0.11199999999999999,15864768,39.12357954545455,46.083243679397526,BEST_adc_episode_048,"[0.625, 0.75, 0.6875, 0.25, 0.1875, 0.75, 0.0625, 0.28125, 0.21875, 0.25, 0.125, 0.6875, 0.40625, 0.421875, 0.453125, 0.265625, 0.4375, 0.125, 0.078125]","[0.6595655251043807, 0.781844580786429, 0.6919754131034916, 0.30977723427495196, 0.2196647857298944, 0.7615489703414283, 0.09489734973844749, 0.2824435786893681, 0.24539869701411268, 0.2561909060976784, 0.13837370592365109, 0.6964644155017895, 0.40948820383311046, 0.4249816100669799, 0.4633926764492405, 0.2784650920861028, 0.44038150762159534, 0.12851462940016, 0.09334077005040495]"
-49,39.92,0.3992,15309504,37.75426136363637,38.192576654115115,BEST_adc_episode_049,"[0.625, 0.625, 0.1875, 0.6875, 0.4375, 0.25, 0.0625, 0.09375, 0.46875, 0.6875, 0.125, 0.40625, 0.15625, 0.328125, 0.234375, 0.140625, 0.78125, 0.515625, 0.375]","[0.6514394621219536, 0.6638856540759274, 0.18877961925998882, 0.7357963899359301, 0.44976138455820824, 0.2780797829978645, 0.08159989150227082, 0.10790735350538955, 0.47866334076114997, 0.717974930550965, 0.14496320907608773, 0.41665269298880886, 0.16565097382453703, 0.33283992317596117, 0.24239950289761497, 0.14153324276907903, 0.7911033192316463, 0.5173921760733542, 0.3791382075735096]"
-50,26.11,0.2611,15494400,38.21022727272727,35.07598171059709,BEST_adc_episode_050,"[0.375, 0.125, 0.5, 0.375, 0.3125, 0.4375, 0.3125, 0.15625, 0.0625, 0.625, 0.5, 0.625, 0.25, 0.1875, 0.296875, 0.671875, 0.546875, 0.46875, 0.3125]","[0.42355211027250367, 0.1515689879848761, 0.5054858237488513, 0.400924349441621, 0.3453495184416325, 0.45730952418396126, 0.33147501980791283, 0.18544188135850148, 0.08362671260440666, 0.6347046722166163, 0.500615342596658, 0.6303691875557929, 0.2561852267087312, 0.19491106946529257, 0.3018790599240663, 0.6787970629114254, 0.5516863331204428, 0.4738309963701837, 0.3163584379948042]"
-51,40.09,0.40090000000000003,20286144,50.02698863636363,35.10623991393222,BEST_adc_episode_051,"[0.1875, 0.625, 0.125, 0, 0.125, 0.125, 0.75, 0.0625, 0.125, 0.09375, 0.03125, 0.40625, 0.1875, 0.078125, 0.734375, 0.5, 0.484375, 0.609375, 0.484375]","[0.18800900353876618, 0.6755216830305663, 0.18749495531187735, 0.06083189664508248, 0.14861359980736924, 0.14402669704093002, 0.7538611119504812, 0.08451257279744209, 0.15049329149661006, 0.11621893300476258, 0.05856807238955557, 0.42918803376166154, 0.20117183095049077, 0.09253731220402928, 0.446525658172589, 0.5043462145295977, 0.23715794192312478, 0.36560054249360524, 0.44503370999805536]"
-52,31.930000000000003,0.31930000000000003,14503680,35.76704545454545,38.35059171597633,BEST_adc_episode_052,"[0.6875, 0.125, 0.6875, 0.125, 0.3125, 0.3125, 0.625, 0.21875, 0.46875, 0.4375, 0.59375, 0.40625, 0.34375, 0.0625, 0.375, 0.625, 0.5, 0.1875, 0.390625]","[0.73395286418096, 0.16754066640387438, 0.7209570875915422, 0.1681884977958772, 0.31805739743848527, 0.3261445159036702, 0.6292414021702366, 0.248810432493258, 0.4792354861711528, 0.4384757887530725, 0.5965449424863919, 0.4259641120144352, 0.35130781855239707, 0.07173720469177929, 0.3750945991709057, 0.6300753101441874, 0.5090481264490533, 0.2021949818677921, 0.3949932666219936]"
-53,29.959999999999997,0.2996,16508736,40.71164772727273,23.164335664335663,BEST_adc_episode_053,"[0.0625, 0, 0.0625, 0.5, 0.0625, 0.375, 0.5625, 0.28125, 0.28125, 0.3125, 0.375, 0.25, 0.71875, 0.15625, 0.765625, 0.578125, 0.625, 0.625, 0.578125]","[0.07881526191243651, 0.0581099494983824, 0.07685621414384462, 0.5211594269354375, 0.08719508495416091, 0.3774155458181325, 0.5665322860540887, 0.29060965698154273, 0.3090113138959829, 0.32226720572323225, 0.40031967156501, 0.259525855025775, 0.7197216217275502, 0.16384841023206598, 0.7728029169673067, 0.5881242118891469, 0.635289124991967, 0.6263502942848841, 0.5875956461789364]"
-54,14.270000000000005,0.14270000000000005,11627136,28.673295454545457,28.994082840236686,BEST_adc_episode_054,"[0.4375, 0.3125, 0.6875, 0.6875, 0.25, 0.5, 0.375, 0.09375, 0.34375, 0.59375, 0.5, 0.75, 0.46875, 0.359375, 0.5, 0.078125, 0.65625, 0.734375, 0.109375]","[0.4425752438052989, 0.36494003584685963, 0.7289142846129906, 0.6890354766031777, 0.2590263531518744, 0.512798962601378, 0.43565528501968265, 0.11295449153890048, 0.371572608862411, 0.6071515343788091, 0.5266792865649865, 0.7783251312568094, 0.4745081728708682, 0.3723932268298414, 0.5121524298030373, 0.0898868193627575, 0.6624705655138268, 0.7470741523905624, 0.12280219755872421]"
-55,28.110000000000003,0.2811,14832576,36.578125,32.45360408821947,BEST_adc_episode_055,"[0.25, 0.0625, 0.125, 0.5, 0.75, 0.125, 0.25, 0.5625, 0.5, 0.375, 0.5, 0.5625, 0.6875, 0.640625, 0.265625, 0.09375, 0.484375, 0.71875, 0.203125]","[0.2881963307079102, 0.1104892864757921, 0.16849750909519873, 0.5367563123476546, 0.7849804989609749, 0.1704259074936918, 0.306443481838354, 0.5864001903558373, 0.5224869908662115, 0.37845087052637466, 0.5045948772394611, 0.562711619740192, 0.7169237564046015, 0.6554335398429225, 0.276121001359766, 0.10848040188263217, 0.4853446020824535, 0.7244655759959249, 0.21062733061768502]"
-56,28.15,0.2815,13209984,32.57670454545455,29.508472296933835,BEST_adc_episode_056,"[0.5625, 0.5625, 0.6875, 0.125, 0.5, 0.6875, 0.125, 0.25, 0.46875, 0.46875, 0.4375, 0.03125, 0.15625, 0.6875, 0.203125, 0.78125, 0.25, 0.4375, 0.578125]","[0.5992384143225589, 0.5674000072467906, 0.7310437248472665, 0.1646068305972821, 0.5491585930696692, 0.7013963933225553, 0.17530689631542687, 0.2519905161138883, 0.48994971602171167, 0.47483611184660063, 0.44751433923879796, 0.054957638872863084, 0.16602815075432462, 0.6927686114101376, 0.21162097598716909, 0.7846114776880234, 0.25773763373380876, 0.4458372220533706, 0.5843895476883171]"
-57,24.39,0.2439,12697920,31.313920454545453,31.72404518558365,BEST_adc_episode_057,"[0.4375, 0.75, 0.3125, 0.75, 0.4375, 0, 0.1875, 0.59375, 0.53125, 0.375, 0.65625, 0.1875, 0.59375, 0.703125, 0.46875, 0.6875, 0.328125, 0.078125, 0.359375]","[0.44385705038468126, 0.7875702960586219, 0.35364266433446934, 0.7770660099820872, 0.488505447726366, 0.05617539722615119, 0.20699706139651652, 0.6128814794911253, 0.5448784552036764, 0.39171425844378344, 0.6847286883290012, 0.1927101134432247, 0.6241332349382955, 0.7034357995643504, 0.479058328082492, 0.69693435461019, 0.33887001449136267, 0.08733987535516755, 0.3656507467351479]"
-58,16.220000000000002,0.1622,14776128,36.43892045454545,43.2994889725659,BEST_adc_episode_058,"[0.75, 0.25, 0.25, 0.625, 0.4375, 0.375, 0.25, 0.6875, 0.71875, 0.5625, 0.09375, 0.5, 0.3125, 0.59375, 0.4375, 0.25, 0.1875, 0.203125, 0.265625]","[0.7774167370975027, 0.3023063253009814, 0.3099406937696728, 0.6667681612210695, 0.43861810382421673, 0.4259125006027981, 0.2818536904409773, 0.70034671336407, 0.7335789047471434, 0.5922567218590961, 0.11441707720191466, 0.5223521318674057, 0.32743971258297727, 0.5974686304677951, 0.4430541862687923, 0.2555596795896627, 0.192190618100457, 0.21235501732941306, 0.2737205596592609]"
-59,44.07,0.4407,16201152,39.953125,40.74435180204411,BEST_adc_episode_059,"[0.25, 0.25, 0.5, 0.3125, 0.4375, 0.4375, 0.0625, 0.59375, 0.5, 0.375, 0.09375, 0.65625, 0.09375, 0.25, 0.5, 0.265625, 0.453125, 0.09375, 0.625]","[0.2613674028919649, 0.2995725581667028, 0.5421462518682525, 0.36460011248375696, 0.47176498127196287, 0.4911317090798929, 0.09765284484466792, 0.6024154191968284, 0.5005153921386967, 0.40477156464559455, 0.11787491746541218, 0.6668449894994817, 0.12446335338480623, 0.25547871213817575, 0.5135194160432079, 0.27969724120698713, 0.46665139660306526, 0.09640282249360371, 0.6335171710289679]"
-60,43.489999999999995,0.43489999999999995,15215616,37.52272727272727,39.81307154384077,BEST_adc_episode_060,"[0.375, 0.3125, 0.5, 0.5625, 0.3125, 0.375, 0.1875, 0.59375, 0.625, 0.15625, 0.625, 0.3125, 0.125, 0.484375, 0.109375, 0.546875, 0.46875, 0.09375, 0.46875]","[0.3865583275767032, 0.33121269004003395, 0.524002035505402, 0.5889921086323936, 0.33211413992288985, 0.3776514836685928, 0.19840313280921196, 0.6145845634735864, 0.654495122740898, 0.17020295168435245, 0.6434716862022283, 0.33468865172651663, 0.14987463947107904, 0.4909701515364921, 0.11364451916537521, 0.5485152515621086, 0.4712365461591394, 0.09680990925151717, 0.47436185086404986]"
-61,36.35,0.3635,15424704,38.03835227272727,37.019230769230774,BEST_adc_episode_061,"[0.3125, 0.75, 0.4375, 0.75, 0.1875, 0.0625, 0.125, 0.5, 0.1875, 0.28125, 0.375, 0.65625, 0.125, 0.453125, 0.34375, 0.640625, 0.59375, 0.234375, 0.078125]","[0.32470802154986755, 0.768711570784606, 0.4661888296926329, 0.7860669658328138, 0.23895662420524125, 0.11276663244320843, 0.17030045809958733, 0.5041976373658252, 0.1955099576921052, 0.2962375644265433, 0.3955073219166827, 0.6719955630575725, 0.13622121904073675, 0.4662006397666049, 0.34530247334411684, 0.6429938735683176, 0.6011157265946434, 0.23748692817671524, 0.09154268092060333]"
-62,15.180000000000005,0.15180000000000005,11874816,29.28409090909091,26.40532544378698,BEST_adc_episode_062,"[0.125, 0.5625, 0.5, 0.4375, 0.1875, 0.75, 0.625, 0.53125, 0.40625, 0.5, 0.375, 0.21875, 0.4375, 0.5, 0.421875, 0.703125, 0.546875, 0.53125, 0.1875]","[0.13912834522874035, 0.5931118999070227, 0.5300280274814632, 0.47288483771661544, 0.21214038539194652, 0.7935666425823017, 0.6583459665859089, 0.5562595133356932, 0.42652368358182596, 0.5080155782059517, 0.38064317136051024, 0.22237552316283643, 0.43889646533092835, 0.5152617542938771, 0.4273294547240984, 0.7137718758026614, 0.5534604929149219, 0.5353907484394245, 0.19995354707396448]"
-63,27.990000000000002,0.27990000000000004,16464960,40.60369318181819,26.469203873050027,BEST_adc_episode_063,"[0.25, 0, 0.125, 0.625, 0.0625, 0, 0.625, 0.53125, 0.53125, 0.34375, 0.25, 0.0625, 0.75, 0.296875, 0.734375, 0.59375, 0.359375, 0.625, 0.21875]","[0.265807124033095, 0.05506811462007555, 0.1409359316663223, 0.6388019874790384, 0.11619260536793714, 0.05688829016295505, 0.6360759921516822, 0.5452211838985681, 0.5393574302254824, 0.36762604082278333, 0.2544603110258593, 0.09234584222046266, 0.7671058776885664, 0.30257008153530307, 0.7455394305321773, 0.6050523764754262, 0.37197689093743586, 0.6282496760192731, 0.22542142011045477]"
-64,29.110000000000003,0.2911,12464640,30.738636363636363,27.064281871974178,BEST_adc_episode_064,"[0.4375, 0.625, 0.3125, 0.75, 0.125, 0.4375, 0.0625, 0.15625, 0.25, 0.5625, 0.28125, 0.71875, 0.75, 0.59375, 0.625, 0.21875, 0.28125, 0.6875, 0.765625]","[0.4506518887979997, 0.6712308409997796, 0.3443683946084335, 0.766101973192642, 0.1826459020054421, 0.4662600131314883, 0.11459890578149239, 0.1615736906078435, 0.27450187757168537, 0.57844961123808, 0.301146262148013, 0.7239797964565163, 0.7802094729911586, 0.5986487474781911, 0.6374060472506159, 0.23093285545366882, 0.2925286567866153, 0.7029608662724043, 0.7781414995841799]"
-65,25.8,0.258,15566400,38.387784090909086,22.969338353953738,BEST_adc_episode_065,"[0.375, 0.25, 0.25, 0.0625, 0.5, 0.0625, 0.0625, 0.6875, 0.375, 0.25, 0.5625, 0.09375, 0.5, 0.78125, 0.796875, 0.171875, 0.75, 0.546875, 0.21875]","[0.4347214780764193, 0.25994940237121844, 0.31034322356432775, 0.06615967828778538, 0.5564248195023238, 0.10672782475822765, 0.0943488592409702, 0.7026897002319175, 0.39479925459939147, 0.26872157748891184, 0.5797052817754201, 0.10980177885168749, 0.5070109605690272, 0.7933251747401684, 0.7973602950628577, 0.18411304466936393, 0.7613148031226136, 0.5496904157356909, 0.2222737752895238]"
-66,28.52,0.2852,13003776,32.06818181818182,24.3006993006993,BEST_adc_episode_066,"[0.125, 0.4375, 0.5, 0.1875, 0.25, 0.125, 0.4375, 0.5625, 0.71875, 0.625, 0.28125, 0.46875, 0.78125, 0.4375, 0.5, 0.46875, 0.625, 0.296875, 0.6875]","[0.13548895136059544, 0.4671376839977616, 0.5097258765826993, 0.2447116059797085, 0.2705033811121331, 0.15828343243459742, 0.4710491629956122, 0.5775643071915251, 0.7410991458374848, 0.6250760184423306, 0.29527911682900987, 0.48001594444730605, 0.7956751243308553, 0.44257317802283475, 0.5109418820443481, 0.473971521974392, 0.6342091342589606, 0.30430682713972296, 0.7011291883850548]"
-67,11.209999999999997,0.11209999999999998,13254336,32.68607954545455,31.058364712210867,BEST_adc_episode_067,"[0.3125, 0.1875, 0.375, 0.25, 0.625, 0.75, 0.4375, 0.15625, 0.5, 0.5, 0.09375, 0.78125, 0.09375, 0.78125, 0.625, 0.171875, 0.234375, 0.5625, 0.5]","[0.33672014767925573, 0.21104750738963254, 0.37837777058787164, 0.2698867944848556, 0.6415274899734623, 0.7662331771965588, 0.48500795502885696, 0.17141686782655655, 0.5304448674021514, 0.5241025128898474, 0.10247957863428041, 0.7835817284214714, 0.11751493483897894, 0.7907817448009571, 0.6310402443823193, 0.17886475236600732, 0.2348858577124604, 0.571395636838703, 0.5152057310331871]"
-68,20.96,0.2096,12671424,31.248579545454547,24.690693921463154,BEST_adc_episode_068,"[0.375, 0.5625, 0.375, 0.1875, 0.6875, 0.25, 0.6875, 0.40625, 0.21875, 0.25, 0.4375, 0.3125, 0.34375, 0.671875, 0.5, 0.703125, 0.59375, 0.390625, 0.53125]","[0.4173691867227466, 0.6220713603132546, 0.43034317616200135, 0.19735257195703068, 0.7357323856505126, 0.28236888540835625, 0.6922794115129289, 0.4364152081082675, 0.22681895590940454, 0.2669271621122997, 0.45456714863547903, 0.312771309069971, 0.3544260990567498, 0.6805204771258128, 0.5030042899942063, 0.7130656592429772, 0.5981997810701098, 0.4011891395342765, 0.541078303173782]"
-69,22.49,0.2249,13397760,33.03977272727273,44.85610543302851,BEST_adc_episode_069,"[0.75, 0.375, 0.3125, 0.4375, 0.625, 0.1875, 0.4375, 0.75, 0.625, 0.71875, 0.75, 0.46875, 0.5, 0.65625, 0.109375, 0.140625, 0.125, 0.421875, 0.078125]","[0.7690582219300532, 0.42387014639080545, 0.35473671705507315, 0.496310124608952, 0.6436174087973895, 0.2009535619026482, 0.4819495719860104, 0.7654940010100387, 0.6561628413523866, 0.7253026267065272, 0.7740104581960987, 0.4826382250323038, 0.5204008709966523, 0.662925331634904, 0.11280167493476802, 0.1412182532359732, 0.13282295287840212, 0.42483257683833664, 0.08864446491267465]"
-70,26.31,0.2631,12321216,30.38494318181818,27.77030661646046,BEST_adc_episode_070,"[0, 0, 0.5625, 0.0625, 0.75, 0.625, 0.625, 0.78125, 0.46875, 0.4375, 0.53125, 0.53125, 0.5625, 0.515625, 0.65625, 0.140625, 0.3125, 0.578125, 0.703125]","[0.06249406497227063, 0.05577931119463851, 0.604355893317437, 0.07797667572276568, 0.7976689425466166, 0.6815591696073432, 0.626143906950801, 0.7909596604120601, 0.49247494789561563, 0.46543308076162737, 0.5498757092080468, 0.5474857997964239, 0.571095614307523, 0.5255391843010568, 0.6654138285618237, 0.14226877975975008, 0.32398846395295217, 0.5922770455235844, 0.7127334998339229]"
-71,46.650000000000006,0.4665000000000001,17612928,43.434659090909086,42.781737493275955,BEST_adc_episode_071,"[0.5625, 0.5625, 0.3125, 0.0625, 0.0625, 0.75, 0.3125, 0.15625, 0.53125, 0.25, 0.09375, 0.40625, 0.28125, 0.515625, 0.203125, 0.59375, 0.1875, 0.078125, 0.65625]","[0.6046519196232277, 0.5824152467842618, 0.3614835823864766, 0.08527591939914858, 0.11021445589085721, 0.7522371885364356, 0.3240739233101429, 0.17727894848249637, 0.5516316787410065, 0.28082641148505305, 0.09965884305178256, 0.4319054343475445, 0.30474366085218546, 0.5231578420312267, 0.2145338704591181, 0.6065296735142534, 0.20256454405584107, 0.09036368201942677, 0.6699273722841701]"
-72,24.139999999999993,0.24139999999999995,10802304,26.639204545454547,23.473641742872513,BEST_adc_episode_072,"[0.3125, 0.0625, 0.75, 0.4375, 0.5, 0.5, 0.4375, 0.75, 0.46875, 0.4375, 0.4375, 0.5, 0.4375, 0.46875, 0.578125, 0.640625, 0.609375, 0.390625, 0.453125]","[0.3174864576838954, 0.10012926783733678, 0.7502814099362191, 0.48616759251040625, 0.543048531181832, 0.545949886615712, 0.4808179734596435, 0.7574578469250604, 0.49494196759521997, 0.4460948037284688, 0.4384344150706813, 0.5030080724018382, 0.4551199493912304, 0.4828520388717905, 0.583039142502322, 0.6498563310701061, 0.6141516802760366, 0.4019591772908293, 0.4630864204013286]"
-73,13.52,0.1352,8918208,21.992897727272727,18.265868746637977,BEST_adc_episode_073,"[0.625, 0.5, 0.75, 0.25, 0.375, 0.0625, 0.625, 0.25, 0.75, 0.71875, 0.4375, 0.6875, 0.78125, 0.640625, 0.59375, 0.328125, 0.5, 0.78125, 0.546875]","[0.6356894280859596, 0.5580497431277749, 0.7948720074264344, 0.29594357056562776, 0.42159198355536603, 0.12273639821643383, 0.6585510137244469, 0.2617499979409937, 0.7550619478110235, 0.7243372037773178, 0.457497372151708, 0.6929064652949454, 0.7992825602454382, 0.656044089555633, 0.5997796492939792, 0.3358070212999181, 0.5088874874234586, 0.792387227111801, 0.5572542882854381]"
-74,26.559999999999995,0.26559999999999995,12345408,30.444602272727273,30.7557826788596,BEST_adc_episode_074,"[0.0625, 0.4375, 0.75, 0.25, 0.6875, 0.6875, 0.5, 0.21875, 0.78125, 0.4375, 0.09375, 0.28125, 0.375, 0.765625, 0.46875, 0.265625, 0.171875, 0.71875, 0.34375]","[0.07598339790863617, 0.4741344804351668, 0.7789458779528476, 0.30400830334284884, 0.7304383427067863, 0.724075827831347, 0.5484901112006622, 0.23452375025412064, 0.789961138334505, 0.45466120194559795, 0.10068234735212933, 0.3076851030704208, 0.3838471506397184, 0.7756560920453653, 0.47916634871270297, 0.26591089791794603, 0.1850250070237607, 0.7297611251729073, 0.3501776518972128]"
-75,30.479999999999997,0.30479999999999996,14658624,36.14914772727273,33.56643356643357,BEST_adc_episode_075,"[0.375, 0.1875, 0.0625, 0.4375, 0.5625, 0.625, 0.4375, 0.25, 0.71875, 0.1875, 0.0625, 0.75, 0.125, 0.6875, 0.390625, 0.109375, 0.59375, 0.453125, 0.34375]","[0.40203198882313407, 0.22230649475452338, 0.08427609546859893, 0.4468862699541293, 0.6035904146607525, 0.6665998097674776, 0.4517721734313984, 0.2792659352339747, 0.7257284289099795, 0.20446171343488395, 0.06705063918748098, 0.7748706381893097, 0.1468960528502687, 0.6884441206001137, 0.39182476245724696, 0.1177236903434025, 0.6015321753425563, 0.4649293734512022, 0.34658034025455087]"
-76,47.24,0.47240000000000004,16515648,40.72869318181818,44.40559440559441,BEST_adc_episode_076,"[0.5625, 0.4375, 0.5625, 0.25, 0.375, 0.375, 0.0625, 0.3125, 0.65625, 0.5, 0.28125, 0.3125, 0.25, 0.546875, 0.34375, 0.265625, 0.140625, 0.25, 0.5625]","[0.580275443666888, 0.45603050267320977, 0.6034822430477577, 0.2671633682707725, 0.3862022040001404, 0.40786335832724485, 0.10643847326835515, 0.3428233278813146, 0.6652590132899084, 0.5095778616182414, 0.29105721190608813, 0.31906112848628465, 0.2593686550844569, 0.5506701976888593, 0.3578894464289104, 0.28116516997357854, 0.14932893861981533, 0.26135322448510356, 0.5678928915687365]"
-77,27.470000000000006,0.27470000000000006,15379200,37.92613636363637,40.15263582571275,BEST_adc_episode_077,"[0.25, 0.375, 0.4375, 0.3125, 0.6875, 0.5, 0.625, 0.28125, 0.28125, 0.09375, 0.09375, 0.75, 0.71875, 0.21875, 0.5625, 0.328125, 0.484375, 0.109375, 0.28125]","[0.2726195035630604, 0.40035310074791025, 0.4637532779943629, 0.3408785844096989, 0.7389387559912964, 0.5610797782003792, 0.6678725676643071, 0.2931438406766801, 0.28611446965591303, 0.12278228650577287, 0.10169135463087559, 0.7618307476521545, 0.7483628055334678, 0.23123759065231037, 0.574049465815815, 0.334550472304037, 0.499792221873717, 0.12211747043036078, 0.2884944511891938]"
-78,22.230000000000004,0.22230000000000005,13340160,32.89772727272727,26.617132867132863,BEST_adc_episode_078,"[0.0625, 0.375, 0.3125, 0.25, 0.3125, 0.625, 0.75, 0.09375, 0.21875, 0.625, 0.1875, 0.71875, 0.65625, 0.203125, 0.390625, 0.765625, 0.25, 0.671875, 0.546875]","[0.06569161599854888, 0.38160676424512474, 0.36075727303598504, 0.2862251682016026, 0.34151043007842674, 0.6559431304578697, 0.7524228214776744, 0.1221417186134152, 0.24963297372113913, 0.6425408129926583, 0.194255687317011, 0.7328380566673539, 0.6629008968852984, 0.20785651857674334, 0.3932973499027159, 0.7682198480313878, 0.25362459263046977, 0.6719420314446851, 0.5510377454692633]"
-79,54.76,0.5476,15231168,37.56107954545455,39.298682087143625,BEST_adc_episode_079,"[0.6875, 0.125, 0.625, 0.1875, 0.625, 0.125, 0.5, 0.59375, 0.6875, 0.15625, 0.21875, 0.34375, 0.09375, 0.171875, 0.171875, 0.578125, 0.296875, 0.578125, 0.625]","[0.716850785309836, 0.1857703150567971, 0.6405538730980397, 0.22599087600715184, 0.6454520790376663, 0.13323302078693133, 0.516300167283164, 0.619224099490874, 0.7132309220664679, 0.180288519665701, 0.2193668934874443, 0.352865281179751, 0.12399842765077583, 0.18268709772209324, 0.18328389001763654, 0.5816029525692782, 0.3015512327446843, 0.590244565384988, 0.6347077706607407]"
-80,27.070000000000004,0.27070000000000005,12575232,31.011363636363637,29.454679935449168,BEST_adc_episode_080,"[0.6875, 0.4375, 0.625, 0.4375, 0.75, 0.1875, 0.1875, 0.21875, 0.15625, 0.59375, 0.125, 0.65625, 0.40625, 0.65625, 0.234375, 0.5, 0.609375, 0.125, 0.765625]","[0.7060101291823047, 0.471190888109026, 0.6656736583492996, 0.47517651782332365, 0.7721888224338462, 0.20858927837366276, 0.1896500226803439, 0.24640232049999736, 0.173617537968747, 0.6166469414356289, 0.13161486287120286, 0.6569141269308759, 0.41164051696103887, 0.6711830972155858, 0.24180236785190104, 0.5069778130113672, 0.6210862993614761, 0.131820570968194, 0.7810959098334025]"
-81,14.459999999999996,0.14459999999999995,10529856,25.967329545454543,25.393356643356647,BEST_adc_episode_081,"[0.75, 0.625, 0.375, 0.5, 0.125, 0.6875, 0.625, 0.15625, 0.25, 0.625, 0.75, 0.1875, 0.71875, 0.296875, 0.484375, 0.484375, 0.734375, 0.390625, 0.46875]","[0.7558843249618628, 0.6501162501932318, 0.43672325946199275, 0.5086804990541707, 0.1789018779650658, 0.7462481187885762, 0.6743811134078528, 0.18712920875934966, 0.25321938840026553, 0.6558584064095603, 0.7805013627304618, 0.2069302305048077, 0.7440401538468214, 0.30242339056395856, 0.4890575409772929, 0.4860475886654647, 0.7387100621961221, 0.39379089129533834, 0.4690107272789891]"
-82,37.18,0.3718,14302080,35.26988636363637,26.442307692307693,BEST_adc_episode_082,"[0.5, 0.0625, 0.6875, 0.6875, 0.1875, 0.125, 0.625, 0.25, 0.1875, 0.5625, 0.09375, 0.28125, 0.21875, 0.59375, 0.671875, 0.046875, 0.640625, 0.78125, 0.65625]","[0.5507765996795976, 0.09317020368580628, 0.7281693750833126, 0.7431345772653415, 0.19448851638390854, 0.16459799458593233, 0.6737374464518573, 0.27128355079915983, 0.21580834770285598, 0.5808253606531822, 0.11295452440739581, 0.3017925373970344, 0.2464141896656192, 0.5998989166812776, 0.6724500826764727, 0.053706564221749076, 0.6436782577993795, 0.7920026170360069, 0.6669623934972575]"
-83,26.919999999999998,0.2692,12125952,29.90340909090909,20.236013986013987,BEST_adc_episode_083,"[0.5625, 0.1875, 0.125, 0.5625, 0.5, 0.125, 0.6875, 0.53125, 0.1875, 0.40625, 0.1875, 0.625, 0.75, 0.375, 0.796875, 0.75, 0.140625, 0.6875, 0.609375]","[0.5806734671994213, 0.2149785368768056, 0.18291484513937464, 0.6215423555605805, 0.5072507659929262, 0.14053408232635586, 0.7478287564166253, 0.5469075728042965, 0.19274865481691825, 0.42463356973341876, 0.21638202471056192, 0.6487798136091213, 0.7685054827064811, 0.37989959088125624, 0.7998788516775889, 0.7564310959722943, 0.14581482194022916, 0.6962283792716215, 0.6156137577668918]"
-84,52.31,0.5231,18796032,46.35227272727273,57.92764927380312,BEST_adc_episode_084,"[0.75, 0.375, 0.125, 0.375, 0.4375, 0.375, 0.6875, 0.125, 0.0625, 0.34375, 0.4375, 0.625, 0.6875, 0.171875, 0.140625, 0.046875, 0.15625, 0.21875, 0.515625]","[0.7573175559326383, 0.4147394046688682, 0.13910708788246118, 0.4282018927095255, 0.4490127763539931, 0.4335648939024423, 0.6877236409880206, 0.1336595031173764, 0.07626483534231178, 0.35388563191144806, 0.45805727606556984, 0.6419307687081485, 0.715790416106081, 0.18363220920015758, 0.14586034948200416, 0.054560563712137286, 0.1589424648323825, 0.2294196325767054, 0.5219211386977667]"
-85,47.440000000000005,0.47440000000000004,16406208,40.45880681818182,49.858795051102746,BEST_adc_episode_085,"[0.5625, 0.4375, 0.125, 0.75, 0.0625, 0.5, 0.375, 0.28125, 0.75, 0.4375, 0.25, 0.40625, 0.21875, 0.125, 0.390625, 0.078125, 0.25, 0.390625, 0.28125]","[0.5917578898161007, 0.45194839598120057, 0.16308213508456193, 0.7917746428142679, 0.09534569455605152, 0.5407724480605763, 0.41128811872126403, 0.30453871468888066, 0.7538464272838938, 0.4588992452961245, 0.2668874904347365, 0.41878085949107213, 0.24559774534451612, 0.13935003917610148, 0.39746568044991015, 0.08495860076735559, 0.2600735552123229, 0.39526839595550867, 0.29153116665999523]"
-86,23.270000000000003,0.23270000000000002,15092352,37.21875,34.07073695535234,BEST_adc_episode_086,"[0.5625, 0.3125, 0.1875, 0.3125, 0.5625, 0.6875, 0.3125, 0.625, 0.03125, 0.21875, 0.1875, 0.5625, 0.3125, 0.40625, 0.546875, 0.703125, 0.265625, 0.21875, 0.609375]","[0.5745787105607955, 0.34574349124037496, 0.21761030794485337, 0.35654527252878065, 0.6115191071428041, 0.7404238265033342, 0.3691213201121535, 0.6498324350918222, 0.05787514966419161, 0.2269300808471392, 0.20747990519544554, 0.5901455121827267, 0.3341154342097071, 0.4162284373256665, 0.5529113668494419, 0.7049899318744615, 0.27006058959872314, 0.22166677752714212, 0.6131790533244685]"
-87,38.82,0.3882,16670016,41.109375,42.61363636363637,BEST_adc_episode_087,"[0.4375, 0.0625, 0.625, 0.0625, 0.4375, 0.4375, 0.5625, 0.78125, 0.71875, 0.21875, 0.0625, 0.0625, 0.65625, 0.265625, 0.125, 0.453125, 0.25, 0.3125, 0.703125]","[0.49556643257793964, 0.12061192004007584, 0.6816731979832094, 0.07274526611216003, 0.4703877022557257, 0.4977966781791387, 0.6137795576250092, 0.7960519901805727, 0.7222454485173487, 0.2198921388054858, 0.06359054777801593, 0.08452887094189426, 0.6801520019043021, 0.26794204374442965, 0.13327861684725303, 0.4649937226227409, 0.2560749564333891, 0.31541174112439685, 0.7097341845619448]"
-88,37.81,0.37810000000000005,14755392,36.38778409090909,30.227945131791284,BEST_adc_episode_088,"[0.5, 0.5625, 0.5, 0.1875, 0.0625, 0.4375, 0.5, 0.28125, 0.0625, 0.5625, 0.125, 0.46875, 0.6875, 0.546875, 0.265625, 0.703125, 0.484375, 0.4375, 0.28125]","[0.5373554984753157, 0.6191514295655731, 0.5368884159114635, 0.19786375667075315, 0.12421691568146401, 0.49609979805170695, 0.5384754943560646, 0.3069010793953027, 0.08157226650103272, 0.570759743386895, 0.1518276518496151, 0.47292038193705416, 0.7077064866855046, 0.5518006684994647, 0.2711713632156184, 0.7168302006898574, 0.48993564191757494, 0.448524210156712, 0.29169664649416394]"
-89,52.61,0.5261,19752768,48.71164772727273,53.11323292092523,BEST_adc_episode_089,"[0.625, 0.375, 0, 0.5, 0.0625, 0.125, 0.25, 0.4375, 0.65625, 0.15625, 0.65625, 0.15625, 0.375, 0.1875, 0.28125, 0.171875, 0.25, 0.140625, 0.421875]","[0.6760200886957543, 0.43234661619460135, 0.05390563749583401, 0.5219598496588628, 0.07875188923234043, 0.17816944176792962, 0.28712378869689015, 0.44433228121824536, 0.6779154438747313, 0.18025844410549477, 0.6642995106620091, 0.18663298241994378, 0.39980335650980375, 0.2023463557610094, 0.28147800618647056, 0.18661852886270752, 0.250264220016874, 0.14192760928756176, 0.4371018200102499]"
-90,35.699999999999996,0.357,14436288,35.60085227272727,30.100188273265193,BEST_adc_episode_090,"[0.5625, 0.75, 0, 0.1875, 0.125, 0.625, 0.3125, 0.65625, 0.25, 0.34375, 0.5, 0.3125, 0.25, 0.15625, 0.71875, 0.09375, 0.4375, 0.765625, 0.734375]","[0.5945792870972784, 0.7980140631671768, 0.05988211188094825, 0.21580236603886974, 0.18087342561001143, 0.6807602338689852, 0.3345812909393027, 0.6651777117158937, 0.28041077411141524, 0.36064447304870256, 0.5203516579713795, 0.3210193656480032, 0.2690915747680721, 0.17079962938201187, 0.7274581290574179, 0.09832274835556934, 0.4466232702346384, 0.7738298510204297, 0.7422426915598712]"
-91,14.590000000000003,0.14590000000000003,11778048,29.045454545454547,30.197686928456157,BEST_adc_episode_091,"[0.125, 0.75, 0.25, 0.125, 0, 0.75, 0.5625, 0.65625, 0.6875, 0.46875, 0.78125, 0.5625, 0.78125, 0.703125, 0.3125, 0.328125, 0.09375, 0.515625, 0.734375]","[0.16402495630990488, 0.770083170119844, 0.2770131931284656, 0.15524282651004115, 0.05623443276669852, 0.7884982731548394, 0.6120756692059316, 0.6865397067253707, 0.7062517825538399, 0.4928956110588763, 0.7947595625313095, 0.5628007556573777, 0.7859259855471716, 0.7052231418070196, 0.3131029611224419, 0.33055881832836753, 0.09521926428754293, 0.528363697398989, 0.7372963138491766]"
-92,29.790000000000006,0.29790000000000005,12383424,30.53835227272727,27.437466379774072,BEST_adc_episode_092,"[0.375, 0.4375, 0.75, 0.0625, 0.1875, 0.6875, 0.5, 0.40625, 0.4375, 0.5, 0.34375, 0.3125, 0.59375, 0.171875, 0.625, 0.5, 0.1875, 0.71875, 0.609375]","[0.402087169009276, 0.44670200787834297, 0.7726124181813075, 0.08230777994019582, 0.20282755368152616, 0.6882882436838255, 0.5238888907741618, 0.4305572828833839, 0.4618262012792932, 0.5309034416538518, 0.34686723257439384, 0.3212409643939853, 0.6170773533471468, 0.18155410705062222, 0.6294502032176844, 0.5155625803666883, 0.19093556014856788, 0.7224156887395623, 0.6169732741458326]"
-93,30.04,0.3004,12756096,31.457386363636363,20.898332436793975,BEST_adc_episode_093,"[0.75, 0.4375, 0, 0.3125, 0.25, 0.125, 0.5625, 0.5625, 0.15625, 0.375, 0.5625, 0.75, 0.71875, 0.484375, 0.484375, 0.75, 0.46875, 0.671875, 0.40625]","[0.7732330219410463, 0.4454829378286108, 0.05282471237606655, 0.33077305969377957, 0.29901846396209053, 0.14377499269483784, 0.5713743347623207, 0.5756803078359594, 0.17368538117327298, 0.37896322635557683, 0.5810030018232437, 0.7512519396265536, 0.7457337427774544, 0.48973568976783993, 0.49191827455717524, 0.7527546061516593, 0.48338116941263426, 0.6750006199289573, 0.41981034191378586]"
-94,19.379999999999995,0.19379999999999994,9923904,24.473011363636363,20.989107046799354,BEST_adc_episode_094,"[0.4375, 0.375, 0.5, 0.375, 0.6875, 0.625, 0.5, 0.625, 0.15625, 0.3125, 0.5625, 0.78125, 0.03125, 0.765625, 0.6875, 0.203125, 0.78125, 0.421875, 0.5]","[0.4851096349802544, 0.39883886049754025, 0.5370033507572699, 0.3890827559768791, 0.727990753931631, 0.6813501506829717, 0.5443403452104725, 0.6384589838671241, 0.18199712004041896, 0.3287474441755277, 0.5800636743864621, 0.783736737887838, 0.05569356152409177, 0.768508377628691, 0.6908387475196245, 0.2089300408214571, 0.7962582034277831, 0.4357764305609649, 0.5084013235744393]"
-95,23.929999999999996,0.23929999999999996,12948480,31.931818181818183,40.3778913394298,BEST_adc_episode_095,"[0.4375, 0.625, 0.375, 0.125, 0.625, 0.1875, 0.5625, 0.6875, 0.625, 0.28125, 0.75, 0.6875, 0.71875, 0.625, 0.265625, 0.46875, 0.046875, 0.0625, 0.484375]","[0.47425748225122344, 0.6783791613923812, 0.3912440948115638, 0.15612881418613017, 0.6603593433919533, 0.24933898252768583, 0.580487846237365, 0.7176358345369878, 0.6428508210644169, 0.2901713356020438, 0.7752839072941615, 0.7166718941982948, 0.7328459484463281, 0.6314392944041357, 0.27455907091764026, 0.4746291424101009, 0.06104680551600514, 0.06640138278696031, 0.49142477117501504]"
-96,17.03,0.1703,11122560,27.428977272727273,25.504303388918775,BEST_adc_episode_096,"[0.4375, 0.6875, 0.625, 0.6875, 0.1875, 0.1875, 0.5, 0.3125, 0.75, 0.53125, 0.125, 0.4375, 0.71875, 0.640625, 0.4375, 0.1875, 0.578125, 0.546875, 0.75]","[0.45097407766816694, 0.7450690016825929, 0.6625691413432221, 0.7175804822861214, 0.23105075019371243, 0.19330509431496112, 0.5267434469407798, 0.32277658256495906, 0.7526234195572458, 0.5380100023546668, 0.13748844940221006, 0.4450772776283334, 0.7215610979675495, 0.6407615035898743, 0.4449796536043127, 0.2019901484914322, 0.5869676950908317, 0.5578813165972188, 0.752320255224681]"
-97,34.13,0.34130000000000005,17222976,42.47301136363637,44.49973103819257,BEST_adc_episode_097,"[0.0625, 0.0625, 0.75, 0.3125, 0.375, 0.0625, 0.1875, 0.59375, 0.75, 0.46875, 0.34375, 0.5625, 0.21875, 0.328125, 0.15625, 0.3125, 0.640625, 0.1875, 0.09375]","[0.12261612462884897, 0.07645613559118997, 0.7852540336983294, 0.3686275780739695, 0.38746300765999936, 0.1226381969425056, 0.20097302750443619, 0.6036826288746537, 0.7617660897551115, 0.4978605272228973, 0.3573426626576661, 0.5709633387234757, 0.22486926933713894, 0.34358874854669613, 0.15776341095923924, 0.32362614717011157, 0.6561058776126095, 0.19244533911470355, 0.09416082992451744]"
-98,24.060000000000002,0.24060000000000004,10609920,26.164772727272727,21.75564819795589,BEST_adc_episode_098,"[0.5625, 0.5625, 0.0625, 0.5, 0.125, 0.5, 0.625, 0.3125, 0.46875, 0.625, 0.625, 0.5625, 0.71875, 0.515625, 0.6875, 0.640625, 0.546875, 0.453125, 0.234375]","[0.5800225709970706, 0.5833704204248267, 0.12031607548153443, 0.5561947605977495, 0.1322671899448013, 0.5510310266526911, 0.6706531420586666, 0.3277003485083627, 0.4736155810982242, 0.6303644231034107, 0.6440164571809391, 0.570199701253635, 0.7442944386737053, 0.5273238529915386, 0.6906439236845576, 0.6416951636642858, 0.561536647458501, 0.4639943000976954, 0.2433741164981565]"
-99,36.160000000000004,0.36160000000000003,16612416,40.96732954545455,34.319526627218934,BEST_adc_episode_099,"[0.25, 0.0625, 0.1875, 0.625, 0.5625, 0.0625, 0.4375, 0.25, 0.09375, 0.375, 0.65625, 0.125, 0.625, 0.203125, 0.28125, 0.515625, 0.71875, 0.515625, 0.203125]","[0.268136976301627, 0.09124091733953213, 0.2369869584864586, 0.6681243905440143, 0.5635709361732814, 0.09701587810082817, 0.47834537126323395, 0.2640306147059136, 0.10675575161148179, 0.3750206107172516, 0.6809344450612647, 0.12839081559962195, 0.6532788321026463, 0.20951979134510232, 0.2845739006493017, 0.5195140607422974, 0.7288815809525361, 0.5182689262081731, 0.2148768139565986]"
-100,30.959999999999997,0.3096,14451840,35.63920454545455,33.07557826788596,BEST_adc_episode_100,"[0.25, 0.4375, 0.0625, 0.1875, 0.25, 0.5625, 0.5, 0.125, 0.5625, 0.625, 0.375, 0.78125, 0.6875, 0.28125, 0.296875, 0.40625, 0.640625, 0.46875, 0.109375]","[0.300189048121282, 0.47775882083065024, 0.07863215492949106, 0.23095124077328874, 0.2726482939783378, 0.6062343769847159, 0.5216324102140082, 0.13002962205685176, 0.5848310952336893, 0.6263925791389491, 0.39599723533092573, 0.7980670725816222, 0.7125263418138874, 0.2873035770082576, 0.31124526063783114, 0.4192328665183532, 0.6412419782858724, 0.4744187627138963, 0.12448670375595043]"
-101,21.799999999999997,0.21799999999999997,11086848,27.340909090909093,18.56845077998924,BEST_adc_episode_101,"[0.375, 0.3125, 0.375, 0.375, 0.5625, 0.25, 0.1875, 0.78125, 0.1875, 0.25, 0.78125, 0.53125, 0.78125, 0.796875, 0.796875, 0.28125, 0.390625, 0.71875, 0.796875]","[0.3871886831647864, 0.3535139376405875, 0.4192783073741625, 0.39439194425012025, 0.6207445289629318, 0.27156474740849884, 0.20772926754462112, 0.800000011920929, 0.21023780614902765, 0.26351853076775034, 0.800000011920929, 0.5395760629870185, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.28662889578178596, 0.4010085092603829, 0.7190329173552752, 0.800000011920929]"
-102,15.639999999999999,0.15639999999999998,4949568,12.205965909090908,11.047606239913932,BEST_adc_episode_102,"[0.75, 0.625, 0.75, 0.4375, 0.6875, 0.75, 0.6875, 0.5, 0.78125, 0.46875, 0.34375, 0.78125, 0.78125, 0.796875, 0.109375, 0.796875, 0.71875, 0.78125, 0.796875]","[0.800000011920929, 0.666163357485093, 0.795967199730801, 0.49630193891774, 0.7271737128536491, 0.7889384899286203, 0.7415847947765571, 0.5223452220416083, 0.800000011920929, 0.4847521590181644, 0.37146780724559125, 0.800000011920929, 0.800000011920929, 0.7995274791509817, 0.12353320983171556, 0.800000011920929, 0.7253976882127076, 0.7905245753315715, 0.800000011920929]"
-103,16.000000000000004,0.16000000000000003,4553280,11.228693181818182,11.397256589564282,BEST_adc_episode_103,"[0.3125, 0.75, 0.5, 0.625, 0.75, 0.75, 0.6875, 0.78125, 0.78125, 0.78125, 0.78125, 0.46875, 0.78125, 0.6875, 0.328125, 0.796875, 0.71875, 0.53125, 0.78125]","[0.3488807941407648, 0.800000011920929, 0.5291729143941855, 0.6433797161088415, 0.800000011920929, 0.800000011920929, 0.7282313506546736, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.49612860657131075, 0.800000011920929, 0.6967165489285053, 0.3364073144412225, 0.800000011920929, 0.7311099928117446, 0.5391346897755777, 0.7898573068315026]"
-104,15.93,0.1593,6857856,16.91193181818182,18.333109198493812,BEST_adc_episode_104,"[0.75, 0.75, 0.75, 0, 0.75, 0.5625, 0.6875, 0.59375, 0.03125, 0.75, 0.75, 0.78125, 0.375, 0.453125, 0.796875, 0.28125, 0.796875, 0.046875, 0.71875]","[0.800000011920929, 0.800000011920929, 0.800000011920929, 0.05000000074505806, 0.7958282556315504, 0.5730011192949565, 0.7345685345267255, 0.6021201721289848, 0.05727680669240054, 0.7613411789727643, 0.7696018362588947, 0.800000011920929, 0.3996128865362702, 0.4677669508061071, 0.800000011920929, 0.2835221049268648, 0.800000011920929, 0.057981557756429436, 0.7275291822889389]"
-105,12.949999999999996,0.12949999999999995,5408640,13.338068181818183,10.519768692845616,BEST_adc_episode_105,"[0.75, 0.4375, 0.5625, 0.5, 0.6875, 0.75, 0.125, 0.78125, 0.5625, 0.6875, 0.78125, 0.40625, 0.78125, 0.59375, 0.734375, 0.703125, 0.796875, 0.453125, 0.765625]","[0.800000011920929, 0.48889227675835867, 0.5956240726472418, 0.5547872108278555, 0.6955509057050445, 0.7694621074560226, 0.13629553488771662, 0.800000011920929, 0.5794462428234807, 0.7127003212028998, 0.800000011920929, 0.4318186520580638, 0.800000011920929, 0.5939735435659027, 0.7376912598391311, 0.7157954141143047, 0.800000011920929, 0.4543716894890447, 0.7691377769816607]"
-106,10.7,0.107,2267712,5.592329545454546,4.743813878429263,BEST_adc_episode_106,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.75, 0.75, 0.78125, 0.78125, 0.78125, 0.796875, 0.796875, 0.796875, 0.78125, 0.796875, 0.75]","[0.800000011920929, 0.800000011920929, 0.789115233922508, 0.7959022498418473, 0.800000011920929, 0.7850093248265936, 0.800000011920929, 0.7858143186030547, 0.7575991785539259, 0.7691129095746011, 0.7937816433073036, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7900845135789127, 0.800000011920929, 0.7651797692826102]"
-107,29.159999999999997,0.29159999999999997,12219840,30.13494318181818,27.057557826788596,BEST_adc_episode_107,"[0.625, 0.75, 0.0625, 0.1875, 0.1875, 0.375, 0.75, 0.40625, 0.5625, 0.28125, 0.78125, 0.25, 0.71875, 0.328125, 0.21875, 0.1875, 0.796875, 0.796875, 0.734375]","[0.6823136694293214, 0.7504025978582628, 0.12235023124992583, 0.19336501359299607, 0.2061080771665862, 0.377570681992159, 0.800000011920929, 0.431125741855958, 0.5669613156852751, 0.2864246784215596, 0.800000011920929, 0.26442321053177653, 0.7278773475254214, 0.3395259266390649, 0.2288201293936203, 0.196330221066235, 0.800000011920929, 0.800000011920929, 0.7457960525755545]"
-108,18.06,0.18059999999999998,7454592,18.383522727272727,13.62627756858526,BEST_adc_episode_108,"[0, 0.5, 0.5625, 0.75, 0.375, 0.5, 0.5, 0.53125, 0.625, 0.78125, 0.78125, 0.6875, 0.25, 0.796875, 0.65625, 0.375, 0.640625, 0.796875, 0.796875]","[0.06067004063496362, 0.5052274421380647, 0.5654691517958715, 0.800000011920929, 0.412761752639613, 0.5559416550121664, 0.5493479988785153, 0.5365573397683194, 0.6431806618735452, 0.7962234294363737, 0.800000011920929, 0.7068204595395087, 0.26629042088758514, 0.800000011920929, 0.6650357198523305, 0.37950457809402727, 0.6555221018344534, 0.800000011920929, 0.800000011920929]"
-109,13.649999999999995,0.13649999999999995,7090560,17.485795454545457,15.747713824636902,BEST_adc_episode_109,"[0.6875, 0.75, 0.6875, 0.5625, 0.4375, 0.6875, 0.75, 0.5625, 0.125, 0.3125, 0.78125, 0.71875, 0.40625, 0.65625, 0.578125, 0.390625, 0.765625, 0.796875, 0.796875]","[0.7413804729421449, 0.800000011920929, 0.7105093981234827, 0.5978796719357898, 0.43869614006093915, 0.6896164854020344, 0.800000011920929, 0.5889467020575934, 0.15231253328092098, 0.3412517931513185, 0.800000011920929, 0.7467558058437848, 0.42851303836524185, 0.6623495314966553, 0.5913287069885518, 0.3931296623146292, 0.7798030327345028, 0.800000011920929, 0.800000011920929]"
-110,19.15,0.19149999999999998,6083712,15.002840909090908,12.110005379236148,BEST_adc_episode_110,"[0.6875, 0.75, 0.6875, 0.75, 0.375, 0.375, 0.75, 0.375, 0.53125, 0.78125, 0.3125, 0.5625, 0.78125, 0.546875, 0.453125, 0.765625, 0.796875, 0.796875, 0.765625]","[0.7007742586078768, 0.800000011920929, 0.7036147817085403, 0.7570403426437622, 0.4194866040656154, 0.41828842766655305, 0.7608486304798361, 0.3862115421769117, 0.547253122238518, 0.800000011920929, 0.3318085850043604, 0.5741627241508777, 0.800000011920929, 0.5576824534858407, 0.45377408760314275, 0.7768382519318306, 0.800000011920929, 0.800000011920929, 0.7774622899609658]"
-111,14.91,0.1491,8549568,21.08380681818182,16.900887573964496,BEST_adc_episode_111,"[0.75, 0.75, 0.625, 0.375, 0.125, 0.6875, 0.25, 0.375, 0.6875, 0.625, 0.03125, 0.6875, 0.625, 0.46875, 0.796875, 0.71875, 0.609375, 0.484375, 0.5625]","[0.800000011920929, 0.800000011920929, 0.6872189645086303, 0.40913182811296833, 0.1492567788817608, 0.706967381278415, 0.3111227542171414, 0.40117835179622746, 0.6899146866411049, 0.6441984268625581, 0.05000000074505806, 0.6999238067179957, 0.6489782880041666, 0.48132894079997474, 0.800000011920929, 0.725709367117687, 0.6187050467256652, 0.49970386042391457, 0.5724955684517538]"
-112,11.470000000000002,0.11470000000000002,2424384,5.978693181818182,5.668370091447014,BEST_adc_episode_112,"[0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78125, 0.78125, 0.75, 0.78125, 0.75, 0.78125, 0.78125, 0.78125, 0.671875, 0.796875, 0.75, 0.796875]","[0.7560632336001134, 0.7690670871193208, 0.800000011920929, 0.800000011920929, 0.7801533505228212, 0.7970027268140967, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7646287885013402, 0.7840827979864029, 0.7783921741748151, 0.800000011920929, 0.7930559869904971, 0.7827001485940881, 0.6760833106845038, 0.800000011920929, 0.7599381432912103, 0.800000011920929]"
-113,13.339999999999996,0.13339999999999996,5663808,13.967329545454547,20.37049488972566,BEST_adc_episode_113,"[0.375, 0.625, 0.75, 0.6875, 0.75, 0.75, 0.6875, 0.78125, 0.78125, 0.78125, 0.71875, 0.78125, 0.78125, 0.40625, 0.125, 0.359375, 0.796875, 0.796875, 0.796875]","[0.3799529080127825, 0.6461042178425929, 0.7599743254039196, 0.7215384749082686, 0.800000011920929, 0.800000011920929, 0.6991231088663077, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7451306724877075, 0.800000011920929, 0.800000011920929, 0.4094718122234291, 0.14004491509617611, 0.37195182551475126, 0.800000011920929, 0.800000011920929, 0.800000011920929]"
-114,17.23,0.1723,6500160,16.029829545454547,10.718128025820334,BEST_adc_episode_114,"[0.0625, 0.6875, 0.375, 0.4375, 0.625, 0.375, 0.75, 0.53125, 0.71875, 0.5625, 0.78125, 0.53125, 0.78125, 0.796875, 0.796875, 0.25, 0.796875, 0.796875, 0.4375]","[0.09948195286723216, 0.7216403211917566, 0.4062849703720749, 0.44202228073301353, 0.6278086546419803, 0.4169149069112492, 0.800000011920929, 0.5312618978627807, 0.7239937522455231, 0.5780342207706607, 0.800000011920929, 0.5412132042664008, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.2530100880053203, 0.800000011920929, 0.800000011920929, 0.45278281798262315]"
-115,28.720000000000002,0.2872,13268736,32.72159090909091,29.80096826250672,BEST_adc_episode_115,"[0.75, 0.625, 0.75, 0.4375, 0.25, 0.25, 0.5, 0.25, 0.15625, 0.5, 0.21875, 0.1875, 0.78125, 0.65625, 0.6875, 0.390625, 0.5, 0.046875, 0.65625]","[0.7833427535707678, 0.6642186023535184, 0.800000011920929, 0.4794688097622787, 0.3119538058576128, 0.26873507758430437, 0.5146144553393278, 0.2795873897889771, 0.1694362883579108, 0.5028132630321529, 0.23814254726996054, 0.21490914670920647, 0.800000011920929, 0.6664494919176935, 0.701786772338125, 0.40421925500433153, 0.5144870521386379, 0.05000000074505806, 0.6658163548650918]"
-116,17.630000000000003,0.1763,7223616,17.813920454545453,14.355836471221087,BEST_adc_episode_116,"[0.625, 0.375, 0.75, 0.4375, 0.5625, 0.75, 0.6875, 0.34375, 0.78125, 0.25, 0.3125, 0.65625, 0.3125, 0.484375, 0.671875, 0.671875, 0.75, 0.796875, 0.796875]","[0.646459368653763, 0.38595488132746947, 0.779698677193086, 0.4671630233863404, 0.5781201082613172, 0.800000011920929, 0.6896176575510161, 0.35647161499174407, 0.800000011920929, 0.25523559396076145, 0.33331251934529316, 0.6769317452636132, 0.3279363316403914, 0.4981192369977042, 0.6826672988591207, 0.6834953941970562, 0.7569898387965199, 0.800000011920929, 0.800000011920929]"
-117,12.11,0.1211,10297728,25.394886363636367,21.083243679397526,BEST_adc_episode_117,"[0.75, 0.5625, 0.125, 0.125, 0.6875, 0.75, 0.0625, 0.6875, 0.03125, 0.78125, 0.125, 0.5, 0.75, 0.453125, 0.796875, 0.5, 0.046875, 0.796875, 0.46875]","[0.800000011920929, 0.5919196387548776, 0.18126146245098929, 0.12996932718983045, 0.735563688307011, 0.800000011920929, 0.07901855456712747, 0.6984080926711996, 0.060979403801893595, 0.800000011920929, 0.1497351940588909, 0.5279734294714127, 0.7690530576462266, 0.4533334339039137, 0.800000011920929, 0.5071327172180606, 0.060996336951481434, 0.800000011920929, 0.4726339087750759]"
-118,10.119999999999996,0.10119999999999996,2462400,6.072443181818182,4.932087143625606,BEST_adc_episode_118,"[0.75, 0.6875, 0.75, 0.6875, 0.75, 0.75, 0.75, 0.78125, 0.75, 0.78125, 0.75, 0.75, 0.78125, 0.796875, 0.796875, 0.765625, 0.796875, 0.796875, 0.75]","[0.800000011920929, 0.7358166340150735, 0.800000011920929, 0.7125153966112641, 0.7728532510616964, 0.7798373036726396, 0.7822173608071713, 0.800000011920929, 0.7612177658649582, 0.7846199804354749, 0.7644152821731781, 0.7688080274842433, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7715551755872867, 0.800000011920929, 0.7974073163872746, 0.7540612589048346]"
-119,19.979999999999997,0.19979999999999998,7451136,18.375,18.013717052178592,BEST_adc_episode_119,"[0.75, 0.125, 0.6875, 0.5625, 0.75, 0.625, 0.5, 0.375, 0.625, 0.65625, 0.1875, 0.78125, 0.28125, 0.578125, 0.796875, 0.671875, 0.515625, 0.390625, 0.671875]","[0.800000011920929, 0.13634812279817643, 0.692709143873233, 0.6048969335394765, 0.800000011920929, 0.6277730813639718, 0.5612770226001889, 0.38991269188075994, 0.652294308834275, 0.6861592094138484, 0.21834606278264101, 0.800000011920929, 0.3115893071549216, 0.5854298449504527, 0.800000011920929, 0.6768806458108845, 0.5191277005805124, 0.40054153731045145, 0.6836215616881536]"
-120,15.600000000000003,0.15600000000000003,5571072,13.738636363636363,12.910166756320601,BEST_adc_episode_120,"[0.375, 0.5625, 0.75, 0.5, 0.75, 0.75, 0.3125, 0.78125, 0.78125, 0.75, 0.25, 0.6875, 0.78125, 0.4375, 0.671875, 0.421875, 0.703125, 0.796875, 0.796875]","[0.39796196991340804, 0.6174762098847743, 0.800000011920929, 0.5402915422435155, 0.7993965613370697, 0.800000011920929, 0.33890058381624716, 0.800000011920929, 0.800000011920929, 0.7638319639105443, 0.2653464020700077, 0.7129228013398521, 0.787852382129087, 0.44409988489620555, 0.6740381711606089, 0.4328426028082542, 0.7116221802226207, 0.800000011920929, 0.800000011920929]"
-121,18.320000000000004,0.18320000000000003,4769280,11.761363636363637,6.505513717052179,BEST_adc_episode_121,"[0.75, 0.5625, 0.5625, 0.5625, 0.125, 0.625, 0.75, 0.59375, 0.78125, 0.75, 0.78125, 0.6875, 0.78125, 0.796875, 0.796875, 0.796875, 0.671875, 0.796875, 0.734375]","[0.800000011920929, 0.5780822637599383, 0.6138678840095203, 0.5776391582274225, 0.1305981372974232, 0.6457008544258893, 0.773813583636948, 0.6176055345645021, 0.800000011920929, 0.7720172186171844, 0.800000011920929, 0.6973174513349494, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.6815567317445461, 0.800000011920929, 0.7420015399571656]"
-122,11.580000000000002,0.11580000000000001,5281920,13.025568181818182,11.565357719203872,BEST_adc_episode_122,"[0.75, 0.5625, 0.625, 0.6875, 0.6875, 0.75, 0.375, 0.25, 0.78125, 0.40625, 0.65625, 0.78125, 0.5625, 0.796875, 0.796875, 0.78125, 0.53125, 0.671875, 0.453125]","[0.7978250288825456, 0.6241964458002489, 0.6261239057270414, 0.7343618329709953, 0.7225662732252716, 0.800000011920929, 0.42536173041316977, 0.2621874600482177, 0.800000011920929, 0.4229331028905934, 0.6799156485315545, 0.800000011920929, 0.5639175248564935, 0.800000011920929, 0.800000011920929, 0.7921116251006239, 0.5357141038762864, 0.6856912264317284, 0.4546742536156487]"
-123,12.270000000000003,0.12270000000000003,7178112,17.701704545454543,9.884346422807962,BEST_adc_episode_123,"[0.6875, 0.75, 0.25, 0.125, 0.75, 0.1875, 0.75, 0.71875, 0.125, 0.65625, 0.59375, 0.40625, 0.78125, 0.796875, 0.734375, 0.671875, 0.796875, 0.796875, 0.640625]","[0.7493219234100876, 0.790857685818717, 0.2624569500141163, 0.13943333879803987, 0.800000011920929, 0.20394235514375203, 0.7847272571421494, 0.7474094974872721, 0.1323761434487103, 0.663069995392087, 0.6201855140292949, 0.4091671208658957, 0.800000011920929, 0.800000011920929, 0.7475969983480445, 0.6736620637594889, 0.800000011920929, 0.800000011920929, 0.6521917899336787]"
-124,13.200000000000001,0.132,3007872,7.417613636363636,6.522323830016137,BEST_adc_episode_124,"[0.6875, 0.6875, 0.75, 0.6875, 0.6875, 0.75, 0.75, 0.78125, 0.65625, 0.78125, 0.71875, 0.75, 0.71875, 0.703125, 0.78125, 0.75, 0.75, 0.734375, 0.796875]","[0.7273166285966929, 0.7335243205084739, 0.7824116371978312, 0.7443792749316529, 0.7488437198288778, 0.7528801222409895, 0.7640296436209567, 0.800000011920929, 0.6772874016577096, 0.781699087210472, 0.7297519445262547, 0.7645546802257576, 0.7212369180987073, 0.706026878830316, 0.7837933408484649, 0.7563958502396411, 0.7504274836298447, 0.7348445082365612, 0.800000011920929]"
-125,51.629999999999995,0.5163,20280960,50.01420454545455,52.545051102743415,BEST_adc_episode_125,"[0.3125, 0.75, 0.75, 0.5625, 0.5, 0.0625, 0, 0.09375, 0.09375, 0.09375, 0.25, 0.375, 0.1875, 0.625, 0.328125, 0.328125, 0.078125, 0.125, 0.46875]","[0.35096780990105014, 0.7744951043500246, 0.800000011920929, 0.5979426750064127, 0.5525745586050471, 0.11610963704154087, 0.05000000074505806, 0.12132609219309759, 0.11934425810572598, 0.11952017305443566, 0.2508085272400707, 0.38972216406483806, 0.20849148545119603, 0.6341028949458588, 0.33409433958764484, 0.33457930550876014, 0.09149950733530353, 0.12809669762198708, 0.05000000074505806]"
-126,65.75,0.6575,20239488,49.91193181818182,37.30500268961807,BEST_adc_episode_126,"[0.3125, 0.375, 0, 0.6875, 0.125, 0.25, 0.125, 0.40625, 0.09375, 0.15625, 0.1875, 0.03125, 0.03125, 0.21875, 0.53125, 0.578125, 0.53125, 0.5625, 0.59375]","[0.3373273659846293, 0.40758805084757177, 0.05087759271075606, 0.7127882902046883, 0.13299973582345143, 0.3041088903936231, 0.1375488939159119, 0.42378423465084764, 0.11705120352530501, 0.18218105486029554, 0.19406607339799897, 0.05000000074505806, 0.05000000074505806, 0.2302640236270768, 0.24402949522532963, 0.07268526121534982, 0.15110049221763155, 0.418017442118951, 0.6054356724763786]"
-127,61.59,0.6159,20283840,50.02130681818182,37.82611619150081,BEST_adc_episode_127,"[0.1875, 0.125, 0.375, 0.75, 0, 0.25, 0.125, 0.375, 0.1875, 0.03125, 0.03125, 0.25, 0.28125, 0.171875, 0.546875, 0.4375, 0.515625, 0.578125, 0.53125]","[0.21527822229276683, 0.16058170459534937, 0.42458355523328956, 0.784637115734031, 0.05000000074505806, 0.30849120224092413, 0.15210651417122373, 0.3955373152416574, 0.20249381261504162, 0.05000000074505806, 0.05000000074505806, 0.26473403890939307, 0.28183246705768394, 0.1761207487414313, 0.5509792861799784, 0.4449557538640485, 0.13352944742520614, 0.3923536317547593, 0.2993522679663047]"
-128,53.580000000000005,0.5358,20275776,50.00142045454545,52.78711672942442,BEST_adc_episode_128,"[0.5, 0, 0.375, 0.3125, 0.3125, 0.5, 0.75, 0.0625, 0.25, 0.1875, 0.1875, 0.40625, 0.09375, 0.046875, 0.296875, 0.078125, 0.1875, 0.75, 0.234375]","[0.5159700905788056, 0.05000000074505806, 0.3771106102512821, 0.3411046910924593, 0.3506777499170915, 0.5038450393860242, 0.7661706081421802, 0.08764226157878327, 0.26015537151603485, 0.2121890140128899, 0.21636667296587686, 0.4244094541343669, 0.09882291001306831, 0.055597581549537645, 0.3048687681828526, 0.08516473220478145, 0.05000000074505806, 0.5391722059607066, 0.05000000074505806]"
-129,50.760000000000005,0.5076,20277504,50.00568181818181,30.567509413663256,BEST_adc_episode_129,"[0.375, 0.125, 0.1875, 0.125, 0.25, 0, 0.375, 0.03125, 0.0625, 0.15625, 0.0625, 0.5625, 0.65625, 0.28125, 0.796875, 0.21875, 0.609375, 0.5, 0.609375]","[0.4246526520226215, 0.13620784077700743, 0.2287735415835037, 0.16241409660046405, 0.25124863623243787, 0.05000000074505806, 0.3764715506548194, 0.05000000074505806, 0.08884936888354265, 0.15751411087344896, 0.08651410149612543, 0.3453641992971599, 0.6569945907581669, 0.12782255670990514, 0.800000011920929, 0.22394251321727923, 0.11387514611855375, 0.26320840301293286, 0.3727680654185823]"
-130,27.92,0.2792,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_130,"[0, 0, 0, 0, 0.0625, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05143937731753917, 0.11915746494149185, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05030084235985256, 0.05000000074505806, 0.05000000074505806, 0.07012229377198743, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-131,55.00000000000001,0.55,20046528,49.43607954545455,32.38636363636363,BEST_adc_episode_131,"[0.125, 0.3125, 0.1875, 0.3125, 0.25, 0.125, 0.1875, 0.25, 0.15625, 0.03125, 0.4375, 0.15625, 0.15625, 0.6875, 0.046875, 0.671875, 0.390625, 0.671875, 0.6875]","[0.13129332090665968, 0.3741324381432902, 0.2062085389854718, 0.3492555949032311, 0.29289183259571644, 0.1645010894602484, 0.24120516142689555, 0.28087791020112723, 0.18309750656023588, 0.05923633067026073, 0.4435041112426162, 0.16360754942274852, 0.18676088159455198, 0.6977275502956126, 0.05000000074505806, 0.5456708805634272, 0.12208092599075637, 0.38993042654900356, 0.6875207635266855]"
-132,66.32000000000001,0.6632000000000001,19895040,49.0625,47.892011834319526,BEST_adc_episode_132,"[0.1875, 0.5625, 0.25, 0.0625, 0.625, 0.125, 0.625, 0.0625, 0.375, 0.03125, 0.28125, 0.09375, 0.21875, 0.375, 0.046875, 0.046875, 0.5, 0.765625, 0.484375]","[0.21377042118846737, 0.5689553421448292, 0.29980701045442154, 0.10321948551162477, 0.650940701718083, 0.15489086616050524, 0.6433516563070317, 0.06537488378339221, 0.3948520820085609, 0.05000000074505806, 0.297980929772879, 0.0979738345641871, 0.23078479485155146, 0.3808605507619181, 0.05000000074505806, 0.05000000074505806, 0.09435658496570713, 0.7679342702749686, 0.4851955789160145]"
-133,39.839999999999996,0.3984,19698624,48.578125,51.42549757934374,BEST_adc_episode_133,"[0.0625, 0.4375, 0.125, 0.3125, 0.5, 0.1875, 0.5625, 0.25, 0.28125, 0.78125, 0.03125, 0.25, 0.03125, 0.328125, 0.609375, 0.140625, 0.390625, 0.046875, 0.0625]","[0.10426173324730656, 0.44830551870455687, 0.13453259339204993, 0.36698500163167314, 0.5099594815175447, 0.23630369730289924, 0.5817140585911347, 0.2795268183971067, 0.3106119068582299, 0.800000011920929, 0.05000000074505806, 0.2672435782374356, 0.05000000074505806, 0.33093125818140207, 0.6155633246319898, 0.14980957678924245, 0.39347459878342206, 0.05000000074505806, 0.07594361515711529]"
-134,32.28,0.32280000000000003,18845568,46.47443181818181,52.72996234534696,BEST_adc_episode_134,"[0.1875, 0.125, 0.75, 0.375, 0.0625, 0.75, 0.625, 0.15625, 0.0625, 0.3125, 0.375, 0.28125, 0.46875, 0.09375, 0.203125, 0.5625, 0.046875, 0.109375, 0.546875]","[0.1988872002592045, 0.14304528713237152, 0.800000011920929, 0.41405502201581595, 0.1041097715359141, 0.800000011920929, 0.6387781604594146, 0.16156484251092987, 0.07391988542273187, 0.34312508088231164, 0.40248032284056745, 0.28770105580101335, 0.4805541408755629, 0.09682129512207169, 0.21220165096166183, 0.5726940066499893, 0.056389692538160396, 0.10972088047919154, 0.5522684877756252]"
-135,55.88999999999999,0.5589,19954944,49.21022727272727,56.633270575578265,BEST_adc_episode_135,"[0.25, 0.0625, 0.5625, 0.25, 0.5625, 0.3125, 0.25, 0.125, 0.375, 0.59375, 0.625, 0.09375, 0.15625, 0.25, 0.0625, 0.375, 0.359375, 0.125, 0.109375]","[0.2697082593247824, 0.10229398048624307, 0.6212398441743666, 0.2878756353258711, 0.5765047381830573, 0.32935641938185367, 0.29736860647844, 0.15197312290701837, 0.3932893942702217, 0.604938893562553, 0.6377811055518109, 0.11989825924548994, 0.17434103948536273, 0.2630442911703872, 0.06372646375127841, 0.38188806064633535, 0.36704370429163385, 0.12980543365211017, 0.12287807332754216]"
-136,37.67,0.37670000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_136,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05280568378384177, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-137,45.9,0.45899999999999996,20293632,50.04545454545455,29.683297471759012,BEST_adc_episode_137,"[0.4375, 0.5, 0.1875, 0.125, 0, 0, 0.0625, 0.09375, 0.5, 0.21875, 0.15625, 0.03125, 0.53125, 0.65625, 0.640625, 0.265625, 0.703125, 0.34375, 0.6875]","[0.49127297134693754, 0.5452486568186284, 0.22871850413502484, 0.17576225173641385, 0.05000000074505806, 0.05000000074505806, 0.06326104016315967, 0.12116714026314743, 0.5075825800289995, 0.23865177739660343, 0.17035115177727028, 0.05000000074505806, 0.3973901629337464, 0.1672582765975005, 0.6549677998980443, 0.2780959546694489, 0.7093463384923531, 0.19319367375945848, 0.14556154914126046]"
-138,52.64,0.5264,20279232,50.00994318181819,39.48359332974717,BEST_adc_episode_138,"[0.0625, 0, 0.4375, 0.3125, 0.0625, 0.125, 0.4375, 0.5, 0.03125, 0.1875, 0.53125, 0.375, 0.15625, 0.375, 0.140625, 0.40625, 0.578125, 0.515625, 0.59375]","[0.08499790021243092, 0.05000000074505806, 0.48519729673554646, 0.34246981741737287, 0.11444435652213403, 0.12571608895109013, 0.4888820228239654, 0.513395728187089, 0.05000000074505806, 0.2172580755304571, 0.5526710585010193, 0.3807101512852583, 0.17560562623010478, 0.37649279992990314, 0.1479510269628272, 0.41513690944011733, 0.19307821013523388, 0.12080679080482154, 0.45249892235879186]"
-139,54.230000000000004,0.5423,20056896,49.46164772727273,46.56737493275955,BEST_adc_episode_139,"[0.125, 0.375, 0.125, 0.0625, 0.25, 0.1875, 0.5, 0.6875, 0.21875, 0.21875, 0.15625, 0.6875, 0.28125, 0.109375, 0.75, 0.15625, 0.171875, 0.21875, 0.328125]","[0.13912434211672592, 0.4137293154041262, 0.16208298524204814, 0.09219795664839667, 0.28264017409190895, 0.2439730520074051, 0.5019593385831247, 0.697680102216115, 0.22632799108543955, 0.2358621828248175, 0.18600404519220376, 0.7049027515267647, 0.29515798068509336, 0.12130882421999531, 0.7571357094900272, 0.16923830243473306, 0.17807839816279372, 0.22980307452006118, 0.34324727986493875]"
-140,42.059999999999995,0.4206,20287872,50.03125000000001,35.56011296395912,BEST_adc_episode_140,"[0, 0, 0.375, 0.3125, 0, 0.25, 0.3125, 0.25, 0.625, 0.125, 0.125, 0.28125, 0.28125, 0.3125, 0.609375, 0.78125, 0.265625, 0.09375, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.43133459827237514, 0.3333236943742254, 0.05000000074505806, 0.25928468737835175, 0.3669307237299813, 0.2775948853134797, 0.6279189523406694, 0.13247675561973588, 0.12615003404068806, 0.3098653533938583, 0.31015620355346096, 0.3129217890290214, 0.6111643762139392, 0.7931305037798593, 0.2773976453669247, 0.05000000074505806, 0.5357133589173653]"
-141,56.97,0.5697,20276928,50.00426136363636,46.167294244217324,BEST_adc_episode_141,"[0.625, 0.1875, 0.5625, 0.125, 0, 0.0625, 0.3125, 0.21875, 0.46875, 0.34375, 0.0625, 0.375, 0.53125, 0.234375, 0.0625, 0.3125, 0.265625, 0.65625, 0.421875]","[0.6506069550638959, 0.1906990285602348, 0.5668721069529254, 0.12544698360937184, 0.05000000074505806, 0.10843517489748274, 0.3200389061940811, 0.244983061962799, 0.48002912724269964, 0.34644339943496966, 0.07216909481842296, 0.37550505330094736, 0.5537379681131817, 0.2495048383854891, 0.06277632736629721, 0.3177601213411708, 0.2696171015713846, 0.0828725729741784, 0.42354469479283985]"
-142,32.220000000000006,0.32220000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_142,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05986370855349288, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06469121923435764, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06691517446690369, 0.05000000074505806]"
-143,30.240000000000002,0.3024,18581760,45.82386363636364,53.61081226465841,BEST_adc_episode_143,"[0.0625, 0.125, 0.75, 0.375, 0.25, 0.25, 0.3125, 0.6875, 0.46875, 0.3125, 0.5625, 0.40625, 0.21875, 0.125, 0.0625, 0.5625, 0.28125, 0.125, 0.109375]","[0.06746322879833222, 0.1448679453897741, 0.7598263185013896, 0.39547013245021767, 0.2715583619373317, 0.2964341420420537, 0.3282583909516093, 0.7144264564547711, 0.49448069309300263, 0.3425046942485667, 0.5715839774833237, 0.4291234774923175, 0.23380791240522109, 0.1363600546314078, 0.07149337358093881, 0.5722748262353586, 0.28887483699603667, 0.12966489351188468, 0.12125454281321521]"
-144,45.52,0.45520000000000005,20155392,49.70454545454545,28.83606777837547,BEST_adc_episode_144,"[0.4375, 0, 0, 0.25, 0.25, 0.125, 0.125, 0.03125, 0.03125, 0.53125, 0.25, 0.21875, 0.5625, 0.5625, 0.53125, 0.578125, 0.53125, 0.5625, 0.671875]","[0.49963242493955606, 0.05000000074505806, 0.05000000074505806, 0.27583960581813266, 0.29668310742251963, 0.13684473176266987, 0.17475555361547504, 0.05000000074505806, 0.05659341177783267, 0.5495081664408377, 0.2629957686925475, 0.22426708476904578, 0.37584819545056136, 0.22562335721697452, 0.1472102283213, 0.25762718098132564, 0.20778008652265262, 0.17373703448680664, 0.6812779212036757]"
-145,48.12,0.48119999999999996,16201152,39.953125,37.30500268961807,BEST_adc_episode_145,"[0.3125, 0.6875, 0.1875, 0.5, 0.375, 0, 0.75, 0.25, 0.28125, 0.28125, 0.28125, 0.3125, 0.0625, 0.484375, 0.359375, 0.09375, 0.6875, 0.15625, 0.796875]","[0.32512523861426135, 0.7367242685133674, 0.19918636306092052, 0.529334077728307, 0.398382838895112, 0.05000000074505806, 0.800000011920929, 0.2758852251778316, 0.30217585663488766, 0.3081854047098805, 0.30725006672893607, 0.33981644704357183, 0.08187181669606408, 0.49868632443980265, 0.371193708334281, 0.09603336053725899, 0.6995059852413119, 0.16718671624217674, 0.800000011920929]"
-146,56.96,0.5696,20286144,50.02698863636363,43.93154922001076,BEST_adc_episode_146,"[0.3125, 0.0625, 0.125, 0.375, 0.5, 0.0625, 0.5, 0.375, 0.5, 0.0625, 0.25, 0.03125, 0.375, 0.0625, 0.25, 0.453125, 0.421875, 0.453125, 0.640625]","[0.31250758988410887, 0.07474845447063352, 0.12939493564203297, 0.39794126402679153, 0.5104645337460234, 0.10975992244859285, 0.5159098521107763, 0.38153181004754866, 0.5297495469426179, 0.08398771692134127, 0.25515734461229006, 0.05000000074505806, 0.4004024066193804, 0.06683607913891973, 0.2532735299160501, 0.467008833858874, 0.4348618720783658, 0.21623525456512155, 0.29030340132832844]"
-147,36.83,0.36829999999999996,17150976,42.29545454545455,41.71933835395374,BEST_adc_episode_147,"[0.25, 0.5, 0.375, 0.25, 0.4375, 0.75, 0.5, 0.21875, 0.03125, 0.34375, 0.03125, 0.4375, 0.28125, 0.15625, 0.671875, 0.0625, 0.734375, 0.15625, 0.109375]","[0.29886620907179345, 0.5308231379978551, 0.39720303869055107, 0.28345929480780746, 0.4673017670572707, 0.7688578120985343, 0.5302881032468266, 0.22984009124544047, 0.05000000074505806, 0.349233186779859, 0.05000000074505806, 0.4649513269276737, 0.29499498295684634, 0.16761293825761536, 0.678419607268196, 0.06482283567160126, 0.7431068743754787, 0.1569990091190236, 0.11494201769906942]"
-148,40.39,0.4039,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_148,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05856376948923015, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0660141417651558, 0.0676986423534665, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05356251802168243, 0.07309457189832666, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05762083060994678, 0.05000000074505806]"
-149,68.27,0.6827,20283840,50.02130681818182,47.417966648735884,BEST_adc_episode_149,"[0.1875, 0.625, 0.125, 0.375, 0.25, 0.25, 0.0625, 0.25, 0.125, 0.375, 0.34375, 0.21875, 0.28125, 0.328125, 0.15625, 0.640625, 0.09375, 0.296875, 0.40625]","[0.21866878266639692, 0.6511872283927587, 0.18744092749038468, 0.3983048608366382, 0.2922252563351555, 0.2908505079552518, 0.07459766273842354, 0.2670350935449534, 0.14973154732290322, 0.39103130752947934, 0.3712303687087925, 0.2244411492741884, 0.2995718671627065, 0.3304670091150701, 0.15953372216486908, 0.6407723910717074, 0.10015125585203721, 0.30412524282793707, 0.3427968467564365]"
-150,50.150000000000006,0.5015000000000001,20288448,50.03267045454546,30.389322216245297,BEST_adc_episode_150,"[0.0625, 0.1875, 0.125, 0.375, 0.0625, 0.1875, 0.0625, 0.28125, 0.09375, 0.03125, 0.21875, 0.5625, 0.59375, 0.265625, 0.71875, 0.3125, 0.703125, 0.40625, 0.625]","[0.11340867345850413, 0.24617513533177515, 0.14279993167511276, 0.3834070590710002, 0.11723910578257152, 0.22688750916226655, 0.11756773686166738, 0.29147007241701167, 0.10005431772808362, 0.05973771564451649, 0.2370047409856571, 0.5893637632456116, 0.5939207572627534, 0.1986782781921227, 0.15030931624778113, 0.08938054824556546, 0.6147021557094461, 0.41336727375958005, 0.4527102841460997]"
-151,55.45,0.5545,20135808,49.65625,39.97781065088758,BEST_adc_episode_151,"[0.1875, 0, 0.75, 0.0625, 0.3125, 0.25, 0.375, 0.15625, 0.1875, 0.03125, 0.21875, 0.1875, 0.34375, 0.140625, 0.109375, 0.765625, 0.328125, 0.640625, 0.609375]","[0.24960699387691587, 0.05000000074505806, 0.800000011920929, 0.06735480755679894, 0.355926788557101, 0.28405024055768985, 0.40577820513452323, 0.16422148044767457, 0.18868261716314072, 0.05000000074505806, 0.24514606760346713, 0.19902773912418495, 0.36144636216630327, 0.15028642437719092, 0.10976652573526227, 0.15854746189062868, 0.33005374644536944, 0.18499706465862284, 0.6133495941503148]"
-152,70.38,0.7038,20279808,50.01136363636364,50.17818719741797,BEST_adc_episode_152,"[0.3125, 0, 0.625, 0.125, 0.625, 0.1875, 0.375, 0.03125, 0.1875, 0.1875, 0.25, 0.375, 0.28125, 0.359375, 0.15625, 0.109375, 0.171875, 0.75, 0.234375]","[0.33123085569555394, 0.05000000074505806, 0.6546554242336953, 0.14867954702095507, 0.6600084005011783, 0.18761629311334138, 0.41237522283721006, 0.06120148266933748, 0.21809129503222943, 0.20103419717347568, 0.27019583742817677, 0.3887422301522738, 0.2889890564040729, 0.3628112746551975, 0.16222661530291557, 0.11552236518526787, 0.08268802404599676, 0.09128623659213148, 0.23387025571338949]"
-153,40.78000000000001,0.4078000000000001,16640640,41.03693181818181,34.35314685314685,BEST_adc_episode_153,"[0, 0.0625, 0.5, 0.75, 0.3125, 0.375, 0, 0.65625, 0.5625, 0.4375, 0.09375, 0.15625, 0.34375, 0.4375, 0.078125, 0.796875, 0.390625, 0.34375, 0.546875]","[0.06131930812439373, 0.08497531357447694, 0.5103325227411958, 0.800000011920929, 0.3311325254399083, 0.4169458147427584, 0.05000000074505806, 0.6745460218187314, 0.5830164567280661, 0.44651867995862654, 0.09770778792267565, 0.1749599563708981, 0.3535775718351766, 0.4529855492484942, 0.08607859501644774, 0.800000011920929, 0.4002358511255934, 0.3513330697028134, 0.5559065971657531]"
-154,31.03,0.3103,20289024,50.034090909090914,25.601802044109732,BEST_adc_episode_154,"[0, 0.0625, 0, 0, 0.0625, 0, 0, 0.03125, 0.71875, 0.28125, 0.71875, 0.28125, 0.71875, 0.34375, 0.6875, 0.375, 0.671875, 0.40625, 0.65625]","[0.05000000074505806, 0.0626322948197605, 0.05000000074505806, 0.05000000074505806, 0.06766530804213246, 0.05000000074505806, 0.05000000074505806, 0.05887941336475162, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.1090742460548258, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-155,58.650000000000006,0.5865,19309824,47.61931818181818,42.33795051102744,BEST_adc_episode_155,"[0.5, 0.3125, 0.25, 0.25, 0.75, 0.0625, 0.1875, 0.15625, 0.03125, 0.40625, 0.15625, 0.3125, 0.09375, 0.140625, 0.171875, 0.71875, 0.109375, 0.71875, 0.25]","[0.5573848365331944, 0.35999876746444626, 0.30301317937816186, 0.26281090008075847, 0.800000011920929, 0.0984533792619629, 0.18781615277994063, 0.1738121213054944, 0.05000000074505806, 0.41918169165356967, 0.1668913829652542, 0.32857929261757585, 0.11083730736896932, 0.14945810217574526, 0.18350623711538594, 0.7263333277471616, 0.11702981096347544, 0.7214307001057201, 0.25721599844540455]"
-156,53.52,0.5352,19380096,47.79261363636363,43.21207638515331,BEST_adc_episode_156,"[0, 0.6875, 0.1875, 0.1875, 0, 0.3125, 0.4375, 0.03125, 0.46875, 0.15625, 0.28125, 0.375, 0.4375, 0.1875, 0.171875, 0.25, 0.734375, 0.53125, 0.09375]","[0.05000000074505806, 0.6940912538698089, 0.21471846794491556, 0.24652350018003155, 0.05000000074505806, 0.31993856163882184, 0.4649144184656321, 0.05908458048086781, 0.4874715376418162, 0.17516168733820928, 0.30749056383125817, 0.391571491685943, 0.46065551931428733, 0.1924841688666648, 0.174210141312423, 0.2540933412355476, 0.746475571957795, 0.5324476479032865, 0.1053851385330386]"
-157,32.82000000000001,0.32820000000000005,20286144,50.02698863636363,29.962345346960735,BEST_adc_episode_157,"[0.125, 0, 0.375, 0.25, 0.125, 0.1875, 0.0625, 0.03125, 0.1875, 0.03125, 0.15625, 0.75, 0.28125, 0.71875, 0.71875, 0.453125, 0.546875, 0.4375, 0.53125]","[0.17946103231741756, 0.05121834557243422, 0.4037639061810722, 0.26047996929656025, 0.1464035746383654, 0.20887558096634354, 0.11313824059646498, 0.05000000074505806, 0.20594493874874617, 0.05485153369789485, 0.18355357583262855, 0.13805145180744033, 0.08686966557619241, 0.23916334543754003, 0.7228463788079504, 0.45485556256227455, 0.5470086776075643, 0.443210037485468, 0.06831004968986355]"
-158,35.31999999999999,0.35319999999999996,16906752,41.69318181818182,38.53886498117267,BEST_adc_episode_158,"[0.375, 0.25, 0.6875, 0.4375, 0, 0.25, 0.125, 0.3125, 0.25, 0.28125, 0.375, 0.6875, 0.625, 0.296875, 0.546875, 0.5, 0.4375, 0.171875, 0.203125]","[0.4293857118151195, 0.30310122691226893, 0.7015713438762703, 0.46706253545596765, 0.05000000074505806, 0.2930325660130729, 0.14445039624797393, 0.33641105786870196, 0.2623540553980917, 0.28450097077919073, 0.38240676363898585, 0.70960794782048, 0.649211850058766, 0.30984623808506456, 0.5487406653406686, 0.5103288994954424, 0.4427284256013465, 0.17508860440842017, 0.21540727578513788]"
-159,65.73,0.6573,20288448,50.03267045454546,51.07584722969338,BEST_adc_episode_159,"[0.4375, 0.3125, 0, 0.625, 0.3125, 0.4375, 0.25, 0.0625, 0.03125, 0.4375, 0.3125, 0.3125, 0.15625, 0.515625, 0.09375, 0.046875, 0.28125, 0.53125, 0.5625]","[0.47038456431438935, 0.35271692586472153, 0.05000000074505806, 0.62889840587952, 0.3339596260460848, 0.48013593018487916, 0.31018515779150996, 0.08583130495616512, 0.05000000074505806, 0.4638343005945911, 0.3375099825625899, 0.3251399661353105, 0.16101857501833913, 0.519155872533409, 0.10123829083119665, 0.05000000074505806, 0.2838674120687407, 0.4561598450406691, 0.273733097474054]"
-160,42.88999999999999,0.42889999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_160,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07538641840203547, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06932495002691322, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-161,47.760000000000005,0.4776,20283264,50.01988636363637,30.520441097364177,BEST_adc_episode_161,"[0.25, 0, 0.25, 0.1875, 0.0625, 0.4375, 0.125, 0.25, 0.15625, 0.15625, 0.0625, 0.34375, 0.40625, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875, 0.390625]","[0.2935783575717167, 0.05000000074505806, 0.2526102584974049, 0.2490098570638325, 0.07226599872194522, 0.4833316591914026, 0.13374759688965399, 0.2754376594850053, 0.16169072504012993, 0.16905900803017548, 0.07558645745408893, 0.3673404271823788, 0.05059000465327055, 0.34117009473748094, 0.20795068466431288, 0.2509601019990428, 0.27005159310396626, 0.0666100680933133, 0.18950827341013315]"
-162,62.339999999999996,0.6234,20306304,50.07670454545454,45.108257127487896,BEST_adc_episode_162,"[0.3125, 0.375, 0.125, 0.125, 0.375, 0.0625, 0.375, 0.375, 0.1875, 0.1875, 0.71875, 0.03125, 0.21875, 0.125, 0.328125, 0.359375, 0.578125, 0.125, 0.546875]","[0.346038511924206, 0.3917999392397691, 0.14573513600707672, 0.18011123047801353, 0.37797587007321753, 0.07430326973435233, 0.4311277673529621, 0.3972259805289938, 0.20498188088219302, 0.19680719113597517, 0.7329977614246576, 0.05000000074505806, 0.2360358266338905, 0.13317298626964216, 0.32925407272689156, 0.3674893843660174, 0.5785948636410583, 0.1341061910201145, 0.29621200224246946]"
-163,43.86,0.4386,20279808,50.01136363636364,35.80554061323292,BEST_adc_episode_163,"[0.1875, 0.375, 0, 0, 0.0625, 0.125, 0, 0.09375, 0.46875, 0.625, 0.375, 0.6875, 0.09375, 0.234375, 0.40625, 0.265625, 0.734375, 0.25, 0.734375]","[0.20587200083971335, 0.40617646708724925, 0.05000000074505806, 0.05000000074505806, 0.08465970973264801, 0.12840540156549135, 0.05000000074505806, 0.1068577597477075, 0.49963523848622315, 0.6261687860566443, 0.38850395903839363, 0.7151514842477031, 0.10314895179897049, 0.24551793031487157, 0.4070459969544767, 0.2005971192618784, 0.3415551440860614, 0.11233132989166945, 0.05000000074505806]"
-164,52.910000000000004,0.5291,20278656,50.008522727272734,48.54760623991393,BEST_adc_episode_164,"[0, 0.75, 0, 0.4375, 0.0625, 0.0625, 0.0625, 0.0625, 0.375, 0.6875, 0.5, 0.34375, 0.03125, 0.375, 0.046875, 0.4375, 0.234375, 0.484375, 0.109375]","[0.051415303418482924, 0.800000011920929, 0.05000000074505806, 0.4709164920681258, 0.11393377282794445, 0.08672192099412027, 0.09961473572241031, 0.06445939515306581, 0.3909190694414068, 0.709807395580616, 0.5122412725660824, 0.3562859153244733, 0.05000000074505806, 0.3895175255735538, 0.062216476233904805, 0.4525451336808783, 0.2399257714350236, 0.49117916499460257, 0.09755641538070342]"
-165,56.769999999999996,0.5677,20276352,50.00284090909091,38.54895104895105,BEST_adc_episode_165,"[0.125, 0.0625, 0.125, 0.125, 0.125, 0.125, 0.1875, 0.125, 0.78125, 0.78125, 0.21875, 0.3125, 0.40625, 0.125, 0.09375, 0.53125, 0.578125, 0.53125, 0.578125]","[0.1505102177196397, 0.07246063426009325, 0.16289690270445392, 0.12921945967493925, 0.1692700073631872, 0.16447976223648528, 0.2040696137477651, 0.14121272354902858, 0.800000011920929, 0.800000011920929, 0.21998838548492092, 0.32286769925448744, 0.4276160083586119, 0.12703114888112962, 0.10382649987815606, 0.23059818746052774, 0.5073341130215296, 0.31924461062539267, 0.23946562605872504]"
-166,32.04,0.3204,20276352,50.00284090909091,25.134480903711676,BEST_adc_episode_166,"[0, 0, 0, 0, 0, 0, 0.0625, 0.1875, 0.75, 0.25, 0.71875, 0.3125, 0.71875, 0.28125, 0.71875, 0.296875, 0.703125, 0.359375, 0.6875]","[0.05125281989485947, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07228842324013462, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08050930623524973, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-167,40.73,0.4073,19755648,48.71875,34.773399677245834,BEST_adc_episode_167,"[0.4375, 0.1875, 0.25, 0.3125, 0, 0.125, 0.25, 0.40625, 0.28125, 0.03125, 0.03125, 0.46875, 0.78125, 0.359375, 0.046875, 0.46875, 0.640625, 0.78125, 0.5625]","[0.4402675343934209, 0.1981779279900966, 0.2513941319647579, 0.3298727354469701, 0.05000000074505806, 0.13767847220711837, 0.2801528201142125, 0.4178953732751818, 0.3097954862371384, 0.05000000074505806, 0.05000000074505806, 0.47223192988687734, 0.800000011920929, 0.3638450269467342, 0.05000000074505806, 0.3411295976315072, 0.20345735019737787, 0.7913274436119582, 0.5711913788318574]"
-168,68.11,0.6811,20196288,49.80539772727273,52.47444862829478,BEST_adc_episode_168,"[0.25, 0.1875, 0.625, 0.3125, 0.5625, 0.1875, 0.0625, 0.46875, 0.28125, 0.53125, 0.03125, 0.1875, 0.125, 0.1875, 0.40625, 0.203125, 0.234375, 0.296875, 0.359375]","[0.25424542196722844, 0.24762715025443505, 0.6667608055714286, 0.37281408796224125, 0.5684134031046754, 0.2065783703632119, 0.08804324538388228, 0.4733786038488628, 0.2891783794399303, 0.5508348023939755, 0.05000000074505806, 0.21138255105827536, 0.12933496365148212, 0.19673735691853214, 0.41279547318465293, 0.21142627505215322, 0.24524824629640135, 0.3119145432787447, 0.3670077568689326]"
-169,63.92,0.6392,19707840,48.60085227272727,55.88690155997848,BEST_adc_episode_169,"[0.5, 0.625, 0.125, 0.4375, 0.625, 0.1875, 0.4375, 0.125, 0.125, 0.15625, 0.03125, 0.6875, 0.15625, 0.4375, 0.296875, 0.234375, 0.25, 0.046875, 0.28125]","[0.524995856379161, 0.6311036264313837, 0.18251994249855674, 0.46836161193293624, 0.6543619310749775, 0.2362541827735073, 0.4698820027112447, 0.14553935319269146, 0.13421319361603717, 0.17194923821442865, 0.05000000074505806, 0.7175976715276194, 0.17861774333586314, 0.43971217529132967, 0.31058901793998506, 0.2473900018433084, 0.2635593019920994, 0.0623989326113682, 0.28483802471502445]"
-170,48.34,0.48340000000000005,18470016,45.54829545454545,54.72364174287251,BEST_adc_episode_170,"[0.0625, 0.5625, 0.5, 0.375, 0.625, 0.4375, 0, 0.5, 0.78125, 0.09375, 0.0625, 0.40625, 0.125, 0.265625, 0.046875, 0.09375, 0.109375, 0.671875, 0.28125]","[0.06844642279361646, 0.6176753294915789, 0.5562486788213753, 0.3930405852478911, 0.6573478782899078, 0.49923224939262106, 0.05000000074505806, 0.5279714635366282, 0.800000011920929, 0.11475047085177448, 0.07167642181086362, 0.41191829389973983, 0.15518640619414634, 0.27881146528099443, 0.05000000074505806, 0.09721074055396003, 0.11576395751876477, 0.6780760054786937, 0.29644142596767953]"
-171,27.22,0.2722,14197824,35.012784090909086,27.306347498655192,BEST_adc_episode_171,"[0.4375, 0.4375, 0.375, 0.125, 0.4375, 0.0625, 0, 0.71875, 0.78125, 0.0625, 0.46875, 0.71875, 0.1875, 0.609375, 0.390625, 0.375, 0.359375, 0.796875, 0.265625]","[0.4986224682132003, 0.44917412234692, 0.42479976073475995, 0.18580347434156297, 0.4636508465004639, 0.09441246479248629, 0.05000000074505806, 0.7372133300193109, 0.800000011920929, 0.0901397522164786, 0.49776950652766827, 0.7244094624649138, 0.21408652306272125, 0.6111930913083599, 0.39964049551156366, 0.3828081869310852, 0.3646646768505257, 0.800000011920929, 0.2796048701092851]"
-172,35.629999999999995,0.35629999999999995,20276352,50.00284090909091,25.134480903711676,BEST_adc_episode_172,"[0, 0, 0, 0, 0, 0, 0.0625, 0.1875, 0.75, 0.25, 0.71875, 0.3125, 0.71875, 0.28125, 0.71875, 0.296875, 0.703125, 0.359375, 0.6875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06715373870527144, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06646641513779429]"
-173,47.58,0.4758,20283264,50.01988636363637,30.58095750403443,BEST_adc_episode_173,"[0.1875, 0.125, 0, 0.1875, 0, 0.4375, 0.3125, 0.34375, 0.03125, 0.375, 0.46875, 0.125, 0.03125, 0.640625, 0.453125, 0.625, 0.46875, 0.625, 0.46875]","[0.24391274069876087, 0.1491968154231787, 0.05592324796685754, 0.23916717054696396, 0.053279245955117974, 0.49999033733302156, 0.3712706525033954, 0.37022976193786095, 0.05000000074505806, 0.3761854520526643, 0.49755837218958016, 0.13298026782281477, 0.051433303112124476, 0.41085269699837645, 0.09424969034721574, 0.28895462949903084, 0.05000000074505806, 0.07447937056373918, 0.1980314065548023]"
-174,35.84,0.35840000000000005,20249856,49.9375,30.01277568585261,BEST_adc_episode_174,"[0, 0.25, 0.0625, 0.1875, 0.0625, 0.4375, 0.1875, 0.4375, 0.15625, 0.03125, 0.125, 0.125, 0.75, 0.6875, 0.1875, 0.75, 0.203125, 0.75, 0.25]","[0.05000000074505806, 0.30820619299442986, 0.07111838924271308, 0.20806770049214246, 0.06255458178091582, 0.4643873988181413, 0.1995358273271829, 0.45667020138135705, 0.17629145243215005, 0.05749511987522557, 0.12927167695101183, 0.13578802330938894, 0.13349223886838174, 0.6988043656148218, 0.05000000074505806, 0.33350021571496147, 0.06091721321693468, 0.13485517067230085, 0.25848391573852947]"
-175,66.31,0.6631,20279232,50.00994318181819,47.33055406132329,BEST_adc_episode_175,"[0.1875, 0.3125, 0.4375, 0.3125, 0.125, 0.3125, 0.375, 0.1875, 0.375, 0.03125, 0.40625, 0.125, 0.40625, 0.109375, 0.125, 0.0625, 0.75, 0.203125, 0.75]","[0.18887080345180957, 0.34687806435640395, 0.49530177007236076, 0.3197389813565286, 0.17747223465473413, 0.3529922272410894, 0.4007375062290969, 0.19474288434684156, 0.4017715446054367, 0.05000000074505806, 0.4370755352839923, 0.14235862906330482, 0.4118103639174273, 0.12491655815415638, 0.13030520989595257, 0.07016864130773147, 0.07399725793269218, 0.1639401249737656, 0.6115509366629]"
-176,42.72,0.42719999999999997,20131200,49.64488636363637,30.591043571812804,BEST_adc_episode_176,"[0.125, 0.4375, 0.75, 0, 0, 0.0625, 0.3125, 0.09375, 0.0625, 0.09375, 0.03125, 0.1875, 0.75, 0.3125, 0.703125, 0.328125, 0.703125, 0.328125, 0.796875]","[0.1469999795603572, 0.4558054654586419, 0.800000011920929, 0.05000000074505806, 0.05000000074505806, 0.08925775660808247, 0.3137455882708995, 0.1016810204926569, 0.08704391133381015, 0.09745420455839958, 0.05000000074505806, 0.18252160938046247, 0.28802879767108197, 0.06860903851504968, 0.2945331313385367, 0.06627041740265321, 0.268705287673624, 0.2740060294570983, 0.800000011920929]"
-177,45.03,0.45030000000000003,20286720,50.028409090909086,29.95562130177515,BEST_adc_episode_177,"[0.0625, 0.3125, 0.1875, 0.1875, 0.1875, 0, 0.25, 0.375, 0.15625, 0.03125, 0.4375, 0.0625, 0.375, 0.6875, 0.4375, 0.625, 0.4375, 0.640625, 0.4375]","[0.11505558884975431, 0.32177155094742904, 0.20226725556932462, 0.2458375721206732, 0.2357295135541077, 0.05000000074505806, 0.2528937895867604, 0.3925235373401733, 0.17723655383791087, 0.05000000074505806, 0.45866368587356393, 0.09111831704503937, 0.3449620416810064, 0.16610891220213053, 0.4520625645486278, 0.6352753813245268, 0.24976094112302188, 0.35815450314747826, 0.05000000074505806]"
-178,32.54,0.32539999999999997,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_178,"[0, 0.0625, 0, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.09160152874892125, 0.05000000074505806, 0.05000000074505806, 0.06027441575852048, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06098551493340919, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05526014907870653, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07988335083382432]"
-179,52.47999999999999,0.5247999999999999,20080512,49.51988636363636,31.83835395373857,BEST_adc_episode_179,"[0.25, 0.6875, 0.125, 0.125, 0.25, 0.125, 0, 0.03125, 0.09375, 0.1875, 0.0625, 0.21875, 0.78125, 0.1875, 0.578125, 0.53125, 0.5625, 0.546875, 0.75]","[0.3109257936265934, 0.7096488350157124, 0.16974146606975132, 0.13287642615009568, 0.29519560694730385, 0.1838813068392986, 0.05000000074505806, 0.05000000074505806, 0.12236374117190169, 0.20024388634228008, 0.09080571669947578, 0.2384261394036742, 0.800000011920929, 0.19864270347558866, 0.27424992008272103, 0.1569194550965193, 0.11269023311749518, 0.19162574309433783, 0.751016953597546]"
-180,57.709999999999994,0.5771,20290752,50.03835227272727,35.929935449166216,BEST_adc_episode_180,"[0, 0.3125, 0, 0.3125, 0.125, 0.375, 0.5, 0.15625, 0.28125, 0.09375, 0.0625, 0.3125, 0.4375, 0.5625, 0.21875, 0.390625, 0.671875, 0.40625, 0.65625]","[0.05000000074505806, 0.3392333307244331, 0.05000000074505806, 0.3371319425925867, 0.17977740931473735, 0.40002460562030684, 0.5181485611071347, 0.18011716465163263, 0.28678718255503066, 0.10034826312627608, 0.07117813205275564, 0.3254224244848917, 0.4477854583764153, 0.5726801196114554, 0.22682027701428953, 0.1564680487511806, 0.07740597240047939, 0.19432023114456742, 0.5619372053986569]"
-181,33.099999999999994,0.33099999999999996,18114624,44.671875,44.035771920387305,BEST_adc_episode_181,"[0.0625, 0.5625, 0.1875, 0.125, 0.3125, 0.25, 0.5625, 0.03125, 0.34375, 0.03125, 0.6875, 0.40625, 0.78125, 0.46875, 0.09375, 0.359375, 0.484375, 0.15625, 0.296875]","[0.09015269438208197, 0.5858527985880259, 0.1891825797720923, 0.17219104863054926, 0.3219680391777625, 0.2926530208701409, 0.5850725235805831, 0.060398231642562264, 0.3462119272806078, 0.05000000074505806, 0.6979645266583967, 0.43648917078065097, 0.800000011920929, 0.4752903309488633, 0.10244521545863972, 0.36595351954822664, 0.4947733418860956, 0.1594359284858286, 0.3118175143852165]"
-182,55.35,0.5535,20281536,50.015625,42.926304464766005,BEST_adc_episode_182,"[0.5, 0, 0.5625, 0.375, 0.125, 0.125, 0.4375, 0.03125, 0.40625, 0.03125, 0.125, 0.34375, 0.125, 0.125, 0.53125, 0.046875, 0.546875, 0.5625, 0.546875]","[0.5185352371294534, 0.05000000074505806, 0.6070141758726623, 0.42287433993569096, 0.18551684877783597, 0.13881288006873527, 0.446423221339553, 0.05000000074505806, 0.41793630396197845, 0.05000000074505806, 0.15321888389500654, 0.36232610440068863, 0.12652027859672224, 0.12772266461696488, 0.5357863610079066, 0.057673529290950085, 0.1946327316926937, 0.3458040778559155, 0.2621944802656142]"
-183,63.36999999999999,0.6336999999999999,20299392,50.05965909090909,44.02904787520172,BEST_adc_episode_183,"[0.5, 0.1875, 0.375, 0.1875, 0.0625, 0.25, 0.0625, 0.375, 0.78125, 0.03125, 0.40625, 0.09375, 0.0625, 0.265625, 0.09375, 0.21875, 0.75, 0.21875, 0.734375]","[0.5563660690172813, 0.23534458154999632, 0.43315467388252543, 0.20853701870281594, 0.0625856328163114, 0.2752089230064816, 0.06299595517282518, 0.4051465660030869, 0.800000011920929, 0.05000000074505806, 0.4236849678725797, 0.10747029103870417, 0.07074362313455315, 0.2722551197023699, 0.10355636560697179, 0.15545230924494283, 0.05000000074505806, 0.09412231589112258, 0.4594245165805604]"
-184,27.969999999999995,0.27969999999999995,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_184,"[0, 0, 0, 0.0625, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.051668421153368255, 0.05000000074505806, 0.05000000074505806, 0.07186210963927718, 0.05000000074505806, 0.05819187222690165, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-185,36.029999999999994,0.36029999999999995,20034432,49.40625,49.48224852071006,BEST_adc_episode_185,"[0.3125, 0.375, 0, 0.5, 0.1875, 0.1875, 0.25, 0.28125, 0.3125, 0.25, 0.125, 0.78125, 0.0625, 0.171875, 0.609375, 0.1875, 0.21875, 0.234375, 0.296875]","[0.3128602527437461, 0.4171763721064976, 0.05000000074505806, 0.5341507935563077, 0.24490929275669993, 0.20810184291660422, 0.30230903542250404, 0.29431387326122105, 0.33821546007271674, 0.2743680249550129, 0.1484697123799527, 0.800000011920929, 0.07105566666793016, 0.17230335773368394, 0.6155870432537693, 0.20067050175960516, 0.2210190049611279, 0.2433219578283501, 0.306742767451996]"
-186,68.61,0.6861,20284992,50.02414772727273,47.43477676169984,BEST_adc_episode_186,"[0, 0.75, 0.375, 0.375, 0.125, 0.1875, 0.375, 0.28125, 0.1875, 0.03125, 0.3125, 0.25, 0.125, 0.296875, 0.234375, 0.09375, 0.46875, 0.640625, 0.4375]","[0.05000000074505806, 0.800000011920929, 0.42005061361593216, 0.4098380215540025, 0.12567280375008444, 0.19138318177877484, 0.4239682285259907, 0.30611472712528615, 0.21701066094085963, 0.05000000074505806, 0.33257272344763683, 0.2739770809698665, 0.1283296375020241, 0.3003135968371502, 0.24951597937867115, 0.0970882454813661, 0.2804346704026338, 0.13532559629461208, 0.3830584213798475]"
-187,64.88000000000001,0.6488,20282688,50.018465909090914,38.192576654115115,BEST_adc_episode_187,"[0.1875, 0.75, 0.25, 0.0625, 0.25, 0.3125, 0, 0.1875, 0.375, 0.28125, 0.0625, 0.0625, 0.03125, 0.109375, 0.5625, 0.546875, 0.5625, 0.53125, 0.578125]","[0.22387026242911418, 0.800000011920929, 0.299927206381255, 0.07026982876291621, 0.302667687496611, 0.315666323216149, 0.05000000074505806, 0.1895683981273466, 0.3839517475200138, 0.29546310724103625, 0.0921110935218608, 0.08251350922522252, 0.05000000074505806, 0.12325742153893825, 0.0767990806362315, 0.05000000074505806, 0.19918958278461724, 0.4105219403678624, 0.32988933330436165]"
-188,59.8,0.598,20283840,50.02130681818182,37.493275954814415,BEST_adc_episode_188,"[0.375, 0.1875, 0.25, 0.3125, 0.125, 0.1875, 0.3125, 0.40625, 0.28125, 0.25, 0.25, 0.03125, 0.125, 0.421875, 0.140625, 0.625, 0.46875, 0.625, 0.46875]","[0.4053403191338475, 0.20276890498385486, 0.25851926842515727, 0.3599830913809553, 0.16913139161466917, 0.20083139184469115, 0.3203793567415491, 0.4163953320366103, 0.2833888877244699, 0.27799917652635536, 0.2671966628533067, 0.05000000074505806, 0.14479681700862418, 0.4255376142538586, 0.15496333543406365, 0.3729894494268284, 0.11595033255340981, 0.3097460787623532, 0.33272040993768726]"
-189,48.72,0.48719999999999997,20289024,50.034090909090914,45.62264658418505,BEST_adc_episode_189,"[0.125, 0.25, 0.375, 0, 0.0625, 0, 0.5625, 0.15625, 0.78125, 0.3125, 0.25, 0.46875, 0.375, 0.046875, 0.1875, 0.046875, 0.625, 0.5625, 0.453125]","[0.1426947288562382, 0.29224868200003723, 0.4123187090258975, 0.05000000074505806, 0.11917447886536674, 0.05000000074505806, 0.5796168244869371, 0.1579780908790886, 0.800000011920929, 0.31748417577180205, 0.25138049730732703, 0.4692184833440882, 0.4015154768421056, 0.05000000074505806, 0.18809816200147428, 0.05000000074505806, 0.5161182801006334, 0.5665451177647187, 0.4461026368550486]"
-190,20.87,0.2087,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_190,"[0, 0, 0.0625, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.07265381223600303, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07045736097130624, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-191,43.64,0.4364,20289600,50.03551136363637,42.34803657880581,BEST_adc_episode_191,"[0.1875, 0.0625, 0.0625, 0.25, 0.75, 0.25, 0.3125, 0.46875, 0.09375, 0.03125, 0.15625, 0.4375, 0.46875, 0.328125, 0.140625, 0.15625, 0.765625, 0.265625, 0.6875]","[0.24337800722235114, 0.10805353287730571, 0.0916508241262032, 0.2733923659836196, 0.800000011920929, 0.29300557644329106, 0.3158606356334286, 0.49364084322223495, 0.12203279690463578, 0.05576774413408565, 0.18629380806798837, 0.4582641818328607, 0.4968111817380551, 0.33862703024786023, 0.14386859841040806, 0.05000000074505806, 0.40769335657911454, 0.27890637631808735, 0.1409276833794477]"
-192,60.099999999999994,0.601,20259072,49.96022727272727,37.378967186659494,BEST_adc_episode_192,"[0.125, 0.5625, 0.25, 0.0625, 0.125, 0.0625, 0, 0.59375, 0.4375, 0.0625, 0.375, 0.03125, 0.5, 0.046875, 0.3125, 0.609375, 0.375, 0.671875, 0.4375]","[0.14209069680573005, 0.5814099968412463, 0.2844975834428956, 0.10081092694145061, 0.16793313001787286, 0.08049904534987185, 0.05000000074505806, 0.6026218218656163, 0.45949350985568627, 0.07137423736740436, 0.4006691630842705, 0.05129203074444989, 0.5282245055141924, 0.05000000074505806, 0.3161975442676112, 0.6204774190367782, 0.2970398133894373, 0.11454116984558532, 0.4527389661127786]"
-193,55.400000000000006,0.554,20255040,49.95028409090909,40.07867132867133,BEST_adc_episode_193,"[0.25, 0, 0, 0.75, 0.1875, 0, 0.5625, 0.09375, 0.59375, 0.0625, 0.28125, 0.03125, 0.21875, 0.078125, 0.15625, 0.765625, 0.421875, 0.546875, 0.59375]","[0.26478410557189, 0.05000000074505806, 0.05000000074505806, 0.7742862101467527, 0.21664066324226583, 0.05000000074505806, 0.6249950896279678, 0.10160417131775486, 0.6184187289298226, 0.08699346858486716, 0.2844057551622806, 0.060694039129437584, 0.2312808020361224, 0.078962832594941, 0.07188227040333037, 0.16639826926041915, 0.4344355817681517, 0.2895252958477019, 0.6079609433588604]"
-194,70.75,0.7075,20276928,50.00426136363636,59.29935449166218,BEST_adc_episode_194,"[0.5, 0.6875, 0.625, 0.25, 0.25, 0.125, 0.3125, 0.65625, 0.3125, 0.625, 0.0625, 0.125, 0.03125, 0.203125, 0.046875, 0.203125, 0.046875, 0.328125, 0.71875]","[0.5508100977465247, 0.7077647488571883, 0.6712742379420831, 0.2553417777005791, 0.2633932346591549, 0.16942540517051116, 0.3389528960116859, 0.6757303869884852, 0.3144390503488912, 0.6323227249720784, 0.06400042935632416, 0.14169427927225414, 0.05671512350073156, 0.21665729446379173, 0.05000000074505806, 0.21701951764506308, 0.05000000074505806, 0.1256541114651931, 0.12843122966736067]"
-195,48.019999999999996,0.48019999999999996,17419392,42.95738636363636,41.7327864443249,BEST_adc_episode_195,"[0, 0.75, 0.375, 0.4375, 0.25, 0.3125, 0.6875, 0.0625, 0.125, 0.4375, 0.28125, 0.21875, 0.03125, 0.328125, 0.78125, 0.265625, 0.265625, 0.4375, 0.078125]","[0.05000000074505806, 0.800000011920929, 0.40656587109587516, 0.4376337544535325, 0.31077296784665515, 0.31836932547552454, 0.7175857323650808, 0.07399499341214533, 0.15161839134770325, 0.4421332148009654, 0.2970027830606542, 0.2473388837254476, 0.05000000074505806, 0.33373087159409287, 0.7852933884034685, 0.2773096800533748, 0.2800733866831739, 0.44806582258185906, 0.07827159502433334]"
-196,37.89,0.3789,20288448,50.03267045454546,25.134480903711676,BEST_adc_episode_196,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.75, 0.21875, 0.71875, 0.34375, 0.6875, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375, 0.640625]","[0.09378367521404092, 0.06051679240267997, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08736040296461964, 0.05000000074505806, 0.09253847472183298, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-197,61.31999999999999,0.6132,19869696,49.0,55.99448628294782,BEST_adc_episode_197,"[0.6875, 0.25, 0.1875, 0.6875, 0.0625, 0.25, 0.0625, 0.34375, 0.09375, 0.71875, 0.15625, 0.4375, 0.34375, 0.1875, 0.046875, 0.40625, 0.046875, 0.1875, 0.53125]","[0.6970061120658916, 0.2835971061283835, 0.24405478684280146, 0.7076575918270872, 0.09227947078641333, 0.2990471708103563, 0.12143858149525427, 0.370281098224252, 0.12190038315805, 0.7279306513587998, 0.17683968515419504, 0.45991400856682757, 0.36247391607133267, 0.1990345030179666, 0.05000000074505806, 0.4093533405679224, 0.05000000074505806, 0.1991452283642952, 0.5377006846659684]"
-198,65.44,0.6544,20286720,50.028409090909086,46.264792899408285,BEST_adc_episode_198,"[0, 0.0625, 0.6875, 0.3125, 0.1875, 0.5625, 0.125, 0.40625, 0.0625, 0.4375, 0.25, 0.09375, 0.03125, 0.40625, 0.046875, 0.203125, 0.578125, 0.5625, 0.515625]","[0.05000000074505806, 0.08187465748405098, 0.7376902856538096, 0.33773862426373835, 0.19115661682327417, 0.5899954699559375, 0.1325622722660426, 0.4113263785742136, 0.07964189997087126, 0.45607097136178476, 0.2548112528072042, 0.12091836951048396, 0.05000000074505806, 0.40999118090292247, 0.05000000074505806, 0.20566185625343225, 0.44929764643507464, 0.5688087479275191, 0.3038919644075933]"
-199,30.95,0.3095,18719424,46.16335227272727,42.48251748251749,BEST_adc_episode_199,"[0.1875, 0.375, 0.125, 0.0625, 0.5, 0.0625, 0.1875, 0.15625, 0.53125, 0.28125, 0.78125, 0.40625, 0.25, 0.1875, 0.75, 0.0625, 0.046875, 0.515625, 0.375]","[0.21688775647587588, 0.4090105642858086, 0.1250621666723203, 0.0952867596247871, 0.5596872975084032, 0.10415217088709243, 0.24172901889224907, 0.17363114527283785, 0.5535344858574052, 0.2851059112616487, 0.800000011920929, 0.43352427816122585, 0.2764707326248114, 0.19503623916793567, 0.7501770512698506, 0.07004475119187124, 0.05000000074505806, 0.5183355096586109, 0.3898354852460575]"
-200,43.85,0.4385,18185472,44.846590909090914,46.23453469607316,BEST_adc_episode_200,"[0, 0.125, 0.3125, 0.75, 0.5625, 0.125, 0.25, 0.21875, 0.125, 0.375, 0.71875, 0.6875, 0.09375, 0.046875, 0.546875, 0.046875, 0.046875, 0.75, 0.15625]","[0.05000000074505806, 0.16584491309785543, 0.33850730208795315, 0.800000011920929, 0.5842616774469666, 0.1479219350490099, 0.26578356400469505, 0.24034460974865857, 0.1392918294873019, 0.3837905676729593, 0.7252023985704283, 0.6962587319490432, 0.10892954140966685, 0.05000000074505806, 0.5514688253203234, 0.05000000074505806, 0.05218839360896506, 0.758996044175181, 0.16838611435753645]"
-201,62.94,0.6294,20298816,50.05823863636364,53.869688004303384,BEST_adc_episode_201,"[0.5625, 0.1875, 0.5, 0, 0.6875, 0, 0.125, 0.6875, 0.71875, 0.375, 0.03125, 0.03125, 0.21875, 0.140625, 0.09375, 0.3125, 0.265625, 0.3125, 0.546875]","[0.5737199810063219, 0.20121385564307975, 0.5290506704212492, 0.05000000074505806, 0.7463536278204517, 0.05000000074505806, 0.17430120117616854, 0.7108058212622279, 0.7456604095308494, 0.38278166681930037, 0.05000000074505806, 0.05000000074505806, 0.229872084473139, 0.15308693296835224, 0.09811792876797423, 0.3195581328805902, 0.27588229152908245, 0.3276422522911623, 0.1286792716497236]"
-202,32.68,0.3268,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_202,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06508833862350114, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08088689162045168, 0.05000000074505806, 0.05000000074505806]"
-203,40.54,0.4054,20280384,50.01278409090909,29.706831629908553,BEST_adc_episode_203,"[0.1875, 0.0625, 0.375, 0.0625, 0.0625, 0, 0.125, 0.125, 0.34375, 0.71875, 0.28125, 0.1875, 0.15625, 0.515625, 0.578125, 0.53125, 0.578125, 0.515625, 0.59375]","[0.20357327707573453, 0.09456455878598455, 0.4246066728921977, 0.12360768887941273, 0.09635025481431701, 0.05000000074505806, 0.1775852405678601, 0.12955909240158853, 0.36263942070711475, 0.7241931561897458, 0.3114038776269745, 0.2180529308974405, 0.1678609951133626, 0.36015199295923145, 0.12932367804093914, 0.4517191546936435, 0.08845273725578055, 0.18891966448914013, 0.22688065809555752]"
-204,48.1,0.48100000000000004,18496512,45.61363636363636,40.15263582571275,BEST_adc_episode_204,"[0.125, 0.5625, 0, 0.5625, 0.5, 0.125, 0.0625, 0.125, 0.28125, 0.09375, 0.28125, 0.53125, 0.4375, 0.125, 0.078125, 0.625, 0.65625, 0.5625, 0.265625]","[0.1385805734151045, 0.6089580828207309, 0.05000000074505806, 0.6149639546086059, 0.5469838356650767, 0.16177085906772487, 0.11490324411923736, 0.14872443063296237, 0.3113444125997622, 0.0950093298262595, 0.3110418209646829, 0.5490941180694445, 0.4398969457841558, 0.12852202029300153, 0.08000363456749582, 0.6254000137385116, 0.6668236516233012, 0.5643286726246812, 0.271879533323439]"
-205,19.53,0.1953,15022080,37.04545454545455,35.09279182356105,BEST_adc_episode_205,"[0.5625, 0.5, 0.375, 0.25, 0.5, 0, 0.375, 0.46875, 0.4375, 0.6875, 0.28125, 0.03125, 0.6875, 0.65625, 0.46875, 0.40625, 0.25, 0.3125, 0.40625]","[0.5917113829398076, 0.5170031165210407, 0.3958597380866673, 0.27690667906204725, 0.545744106282869, 0.05000000074505806, 0.3907653378354403, 0.4694332009624839, 0.4425304400023812, 0.7007448360418668, 0.2904736769372054, 0.05000000074505806, 0.691298697670251, 0.660839745490578, 0.4829399887015069, 0.4075378886702107, 0.25846777685670164, 0.3154777393740996, 0.4073423391661022]"
-206,41.06999999999999,0.41069999999999995,20284992,50.02414772727273,29.511834319526624,BEST_adc_episode_206,"[0.25, 0.1875, 0.125, 0.0625, 0.25, 0.0625, 0.1875, 0.15625, 0.03125, 0.03125, 0.40625, 0.6875, 0.40625, 0.65625, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375]","[0.2596010477196122, 0.2400021128049138, 0.16889567399892075, 0.08037923503564509, 0.2637375491969233, 0.0743719288434688, 0.23177740484317627, 0.18152549628016457, 0.05000000074505806, 0.05000000074505806, 0.25692897035002954, 0.42215677876852087, 0.4243837530908453, 0.33964331161315287, 0.27304155336136576, 0.3909540166442575, 0.05000000074505806, 0.05000000074505806, 0.2364307821533151]"
-207,53.459999999999994,0.5346,20281536,50.015625,41.58485745024206,BEST_adc_episode_207,"[0, 0.3125, 0.125, 0.375, 0, 0.125, 0.3125, 0.125, 0.125, 0.78125, 0.0625, 0.3125, 0.75, 0.046875, 0.3125, 0.4375, 0.203125, 0.515625, 0.59375]","[0.05000000074505806, 0.3745536121325688, 0.14985057481853295, 0.3829097262434473, 0.05000000074505806, 0.1593360915034345, 0.33328387158354866, 0.14238671212859105, 0.14460273313086666, 0.800000011920929, 0.06398745185677494, 0.3320808106781175, 0.753188202344961, 0.05000000074505806, 0.31352194740949846, 0.4500048249906731, 0.2097447248723261, 0.05000000074505806, 0.25763289817259083]"
-208,31.44,0.3144,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_208,"[0, 0, 0, 0, 0, 0.0625, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08821843291645402, 0.05000000074505806, 0.053224553384873856, 0.12997689758126074, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-209,62.09,0.6209,18674496,46.05255681818182,40.47538999462076,BEST_adc_episode_209,"[0.5, 0.1875, 0.125, 0.5625, 0.4375, 0.1875, 0.3125, 0.5, 0.40625, 0.03125, 0.25, 0.1875, 0.15625, 0.296875, 0.46875, 0.421875, 0.40625, 0.28125, 0.65625]","[0.5023200880482879, 0.193933109345264, 0.12832002371748183, 0.5792749451642605, 0.45120416564978194, 0.23063393537741747, 0.3128146587942438, 0.5114906851081609, 0.4353241747933156, 0.05000000074505806, 0.27823747316116454, 0.20254940506837518, 0.18729999965082283, 0.29696864613172363, 0.4694530561400365, 0.42616602239661877, 0.41190321347183456, 0.28193236680571016, 0.6573795916689217]"
-210,34.48,0.3448,18353088,45.25994318181818,47.0817643894567,BEST_adc_episode_210,"[0.6875, 0.4375, 0.25, 0.25, 0.4375, 0.3125, 0.4375, 0.25, 0.25, 0.28125, 0.34375, 0.125, 0.5, 0.46875, 0.28125, 0.703125, 0.140625, 0.09375, 0.109375]","[0.7039406820709648, 0.4396891599230087, 0.2976637692406056, 0.3027409172571097, 0.4806817551873361, 0.31437680418810116, 0.4559045885471633, 0.2710359756471508, 0.25335280279822514, 0.2924844612805495, 0.37473266732598487, 0.15494548358013643, 0.5042576958632787, 0.47169999330709556, 0.284303523722406, 0.7091137610560094, 0.1461040355942845, 0.09841900764865905, 0.12348040650989492]"
-211,49.5,0.495,20291904,50.04119318181818,31.39792899408284,BEST_adc_episode_211,"[0, 0.25, 0, 0, 0, 0.625, 0.75, 0.3125, 0.03125, 0.25, 0.21875, 0.28125, 0.15625, 0.5, 0.71875, 0.203125, 0.703125, 0.34375, 0.6875]","[0.05000000074505806, 0.2986205558820392, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.6715178363319989, 0.7578798598678532, 0.3321740327017903, 0.05251912555234607, 0.27251806350688473, 0.2396252473337216, 0.2967447064059113, 0.16010586875562055, 0.44153454461946584, 0.7252829951331606, 0.21571428128354347, 0.2639232858841329, 0.05378147044715007, 0.45039370853535265]"
-212,26.69,0.2669,15657984,38.61363636363636,34.66917697686929,BEST_adc_episode_212,"[0.0625, 0.6875, 0.375, 0.3125, 0.375, 0.5, 0.0625, 0.53125, 0.03125, 0.21875, 0.03125, 0.78125, 0.03125, 0.796875, 0.546875, 0.78125, 0.203125, 0.1875, 0.25]","[0.06878841283612061, 0.6878801117993242, 0.38365601704732505, 0.3169890653962075, 0.40590867767551764, 0.5011426248512232, 0.09222422215425943, 0.5594795212190465, 0.05829068613559914, 0.24064578836999134, 0.05000000074505806, 0.800000011920929, 0.05151092861345108, 0.800000011920929, 0.5570623852952236, 0.789796144096919, 0.215043329233242, 0.19828295999233153, 0.26326745909287264]"
-213,50.42,0.5042,20043648,49.42897727272727,50.89766003227542,BEST_adc_episode_213,"[0.625, 0.3125, 0.125, 0.3125, 0.625, 0.6875, 0.1875, 0.25, 0.0625, 0.125, 0.3125, 0.21875, 0.0625, 0.296875, 0.21875, 0.15625, 0.078125, 0.75, 0.640625]","[0.6349366453557093, 0.32628437320889825, 0.14240028105831717, 0.32145461136999975, 0.6726096194579859, 0.7109180158876066, 0.22378379858469777, 0.27699200270439084, 0.08987978252053856, 0.13167879441361327, 0.32707824768725624, 0.23636803700938097, 0.0752611062499117, 0.3081706732524291, 0.23290244117190093, 0.16522332907691217, 0.08669432688485387, 0.16824084653219795, 0.6543016395450254]"
-214,36.629999999999995,0.36629999999999996,20289024,50.034090909090914,25.601802044109732,BEST_adc_episode_214,"[0, 0, 0, 0.0625, 0, 0.0625, 0, 0.03125, 0.71875, 0.28125, 0.71875, 0.28125, 0.71875, 0.34375, 0.6875, 0.375, 0.671875, 0.40625, 0.65625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07060465106191668, 0.05000000074505806, 0.11220084168771373, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06048762062821348, 0.05134007898887924, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.10265378943128538, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07052251797568176]"
-215,45.550000000000004,0.4555,20276352,50.00284090909091,60.17348036578806,BEST_adc_episode_215,"[0.125, 0.4375, 0, 0.375, 0.4375, 0.1875, 0.1875, 0.5625, 0.78125, 0.28125, 0.40625, 0.59375, 0.03125, 0.1875, 0.125, 0.046875, 0.40625, 0.125, 0.125]","[0.1403734261231191, 0.4390125831866786, 0.05000000074505806, 0.38721917297673253, 0.47943090098439045, 0.21476460122355578, 0.193301667289229, 0.5881860785290023, 0.800000011920929, 0.2935328993509269, 0.43203783059122797, 0.604943214050401, 0.05000000074505806, 0.1965424063639229, 0.13928983463745404, 0.05000000074505806, 0.4216839332228128, 0.1307733906180448, 0.11754396035680242]"
-216,55.790000000000006,0.5579000000000001,19941120,49.17613636363637,39.265061861215706,BEST_adc_episode_216,"[0.125, 0.0625, 0.0625, 0.4375, 0.5625, 0.75, 0.125, 0.5, 0.03125, 0.125, 0.28125, 0.0625, 0.03125, 0.15625, 0.21875, 0.734375, 0.28125, 0.71875, 0.796875]","[0.17216340537319527, 0.09131271879591746, 0.0768206537667246, 0.45429980155553956, 0.567202353708115, 0.7667707081780871, 0.15212425808527855, 0.527694794023895, 0.05000000074505806, 0.15022575217282438, 0.3056437353567417, 0.0927540270368459, 0.05000000074505806, 0.1570812794830331, 0.05000000074505806, 0.05000000074505806, 0.2904702954868538, 0.7062326222375558, 0.800000011920929]"
-217,54.089999999999996,0.5408999999999999,20284992,50.02414772727273,44.74179666487359,BEST_adc_episode_217,"[0.3125, 0, 0.3125, 0.75, 0.125, 0, 0.125, 0.0625, 0.46875, 0.28125, 0.0625, 0.65625, 0.15625, 0.203125, 0.265625, 0.046875, 0.625, 0.46875, 0.625]","[0.3279754697961353, 0.05000000074505806, 0.3698429423223238, 0.800000011920929, 0.12875095844057854, 0.05000000074505806, 0.16440806710659298, 0.07182128875107485, 0.4973833346527756, 0.29731192864043254, 0.06264507012422615, 0.6830077095628614, 0.17023271850025043, 0.21649120678833855, 0.2675166128426894, 0.05000000074505806, 0.4511292531393732, 0.16284629951510343, 0.494694575728]"
-218,49.83,0.49829999999999997,20280960,50.01420454545455,32.917563206024745,BEST_adc_episode_218,"[0.4375, 0.125, 0.0625, 0.0625, 0.0625, 0.375, 0.0625, 0.09375, 0.1875, 0.3125, 0.59375, 0.34375, 0.125, 0.0625, 0.78125, 0.796875, 0.09375, 0.59375, 0.515625]","[0.48583562988644285, 0.13349800466667341, 0.08119270646916321, 0.06760574768275147, 0.101217286989912, 0.4060145907359074, 0.0971495743104307, 0.09674360539440244, 0.21379738948096466, 0.3367045736143668, 0.6085822504943433, 0.35237776567740486, 0.1424892381599941, 0.05000000074505806, 0.30720174913149706, 0.800000011920929, 0.10889406594958803, 0.34946385946908815, 0.2822430309830977]"
-219,47.77,0.4777,19332864,47.67613636363636,36.63259817105971,BEST_adc_episode_219,"[0.125, 0.4375, 0, 0, 0.5, 0.5, 0.0625, 0.21875, 0.59375, 0.125, 0.03125, 0.25, 0.375, 0.796875, 0.40625, 0.265625, 0.078125, 0.796875, 0.0625]","[0.1298697378159023, 0.47533343344488194, 0.05000000074505806, 0.05000000074505806, 0.512533237440714, 0.5054788448043939, 0.12475295027211336, 0.24363554291899775, 0.6071623816810031, 0.153698578096914, 0.0533477405531695, 0.2783345394946764, 0.3812273804992312, 0.800000011920929, 0.4066513783429627, 0.27455399475768016, 0.08241254710983577, 0.800000011920929, 0.07751885129323448]"
-220,29.810000000000002,0.29810000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_220,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.06123167713629448, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.060405721949380924, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09164505104035439, 0.08460108388308801, 0.05000000074505806]"
-221,33.940000000000005,0.33940000000000003,19045440,46.96732954545455,46.26143087681549,BEST_adc_episode_221,"[0.3125, 0.25, 0.0625, 0.75, 0.75, 0, 0.0625, 0.21875, 0.09375, 0.0625, 0.6875, 0.0625, 0.75, 0.796875, 0.046875, 0.28125, 0.296875, 0.34375, 0.046875]","[0.35202255322552, 0.25255549249361114, 0.07860497144449877, 0.800000011920929, 0.800000011920929, 0.05000000074505806, 0.07640077021452395, 0.24594152078740886, 0.09461833450130709, 0.0744633199575459, 0.7057869192630496, 0.0836602367626564, 0.777275473392994, 0.800000011920929, 0.05000000074505806, 0.28127214030811915, 0.3065168575787495, 0.35085838129833696, 0.05000000074505806]"
-222,56.06,0.5606,20275200,50.0,41.06374394835933,BEST_adc_episode_222,"[0.125, 0.0625, 0.4375, 0, 0.25, 0.25, 0, 0.4375, 0.78125, 0.15625, 0.46875, 0.09375, 0.125, 0.203125, 0.15625, 0.5625, 0.203125, 0.734375, 0.265625]","[0.13396407407026936, 0.09286603031329631, 0.4789873195694819, 0.05000000074505806, 0.2873414915780305, 0.2810428379515666, 0.06174967876893318, 0.4386488376603839, 0.800000011920929, 0.1810210001738922, 0.4808366518823844, 0.09558763151167761, 0.14853916939170475, 0.20919820259631788, 0.1704167878652284, 0.5779146755224114, 0.2094293084053667, 0.5191221998827211, 0.09608348026558415]"
-223,37.93,0.37929999999999997,20275776,50.00142045454545,29.508472296933835,BEST_adc_episode_223,"[0, 0.25, 0.375, 0, 0.125, 0.1875, 0, 0.15625, 0.03125, 0.21875, 0.25, 0.59375, 0.5625, 0.53125, 0.578125, 0.515625, 0.59375, 0.5, 0.609375]","[0.05000000074505806, 0.27200434441356375, 0.4228532154418955, 0.05000000074505806, 0.1651190570401316, 0.2474668067294111, 0.06073492073799585, 0.18209496433346406, 0.05000000074505806, 0.23638181483911846, 0.279286425133627, 0.23717717006532382, 0.05000000074505806, 0.05000000074505806, 0.29482554769844094, 0.26336408658178073, 0.05000000074505806, 0.11408464349051968, 0.05000000074505806]"
-224,59.68,0.5968,20279232,50.00994318181819,39.786175363098444,BEST_adc_episode_224,"[0.0625, 0.75, 0.375, 0.1875, 0, 0.4375, 0.0625, 0.25, 0.0625, 0.03125, 0.15625, 0.34375, 0.15625, 0.453125, 0.171875, 0.515625, 0.484375, 0.609375, 0.5]","[0.07990283935270322, 0.7561556865759179, 0.4350473805784147, 0.19615460744968108, 0.05000000074505806, 0.49285474875268637, 0.09239187083956119, 0.25587566886368107, 0.07781370730836444, 0.05000000074505806, 0.1658015264673221, 0.3744751425401962, 0.18026627110878987, 0.4541706904541508, 0.1792873744022513, 0.5261467336961472, 0.36570447448642107, 0.05000000074505806, 0.23508932818577263]"
-225,54.96,0.5496,20293632,50.04545454545455,35.049085529854764,BEST_adc_episode_225,"[0, 0.0625, 0.4375, 0, 0.125, 0.3125, 0, 0.15625, 0.71875, 0.21875, 0.03125, 0.28125, 0.625, 0.28125, 0.40625, 0.234375, 0.734375, 0.265625, 0.71875]","[0.05000000074505806, 0.11325490152787164, 0.4525100638953667, 0.05000000074505806, 0.13767042555037537, 0.3281851367078158, 0.05000000074505806, 0.18517310960651054, 0.7242413981199276, 0.22771353657713816, 0.05415723727892779, 0.3077045127873656, 0.6509793820142565, 0.2870134901794375, 0.4149193100279482, 0.05424781319191431, 0.21905112857376977, 0.1957014626359062, 0.07176923465260807]"
-226,36.8,0.368,20288448,50.03267045454546,25.578267885960194,BEST_adc_episode_226,"[0, 0, 0, 0.0625, 0.0625, 0, 0, 0.0625, 0.6875, 0.375, 0.6875, 0.34375, 0.6875, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375, 0.640625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09676174127763403, 0.07572467063586798, 0.05000000074505806, 0.05000000074505806, 0.06549191413105517, 0.058760363864580986, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0516796065994985, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-227,45.29,0.45289999999999997,20290176,50.03693181818182,27.42401828940291,BEST_adc_episode_227,"[0.125, 0, 0.1875, 0.125, 0.0625, 0, 0.0625, 0.15625, 0.3125, 0.25, 0.53125, 0.5625, 0.5625, 0.546875, 0.5625, 0.53125, 0.578125, 0.53125, 0.5625]","[0.1722789113372543, 0.05000000074505806, 0.24108884758376115, 0.1760891958023254, 0.06539651109736613, 0.05000000074505806, 0.09834026332939247, 0.15771771047481556, 0.3342721891708333, 0.2754605192880986, 0.14167432682726264, 0.05382622620431824, 0.26755418727180713, 0.2985278097830323, 0.23057310224206604, 0.4484643141557288, 0.06043045004389622, 0.07075561787221778, 0.2574512231566323]"
-228,54.099999999999994,0.5409999999999999,20286720,50.028409090909086,33.57651963421194,BEST_adc_episode_228,"[0.1875, 0.0625, 0.25, 0.125, 0, 0.3125, 0.3125, 0.4375, 0.21875, 0.25, 0.46875, 0.03125, 0.28125, 0.203125, 0.53125, 0.578125, 0.515625, 0.59375, 0.5]","[0.22630350930031806, 0.10791413284035142, 0.25849462905211285, 0.12763059174157826, 0.05090419652000953, 0.33485109091130555, 0.34232264590709915, 0.43921875194520604, 0.2463305663847892, 0.2500578447444831, 0.49740637085551087, 0.05000000074505806, 0.2856237101798355, 0.21315668792975362, 0.226040767842223, 0.23120519156632408, 0.05000000074505806, 0.05000000074505806, 0.1689761802434453]"
-229,47.11,0.4711,20280384,50.01278409090909,29.20252824098978,BEST_adc_episode_229,"[0.125, 0.125, 0, 0.1875, 0.25, 0.0625, 0.375, 0.03125, 0.09375, 0.09375, 0.53125, 0.25, 0.71875, 0.359375, 0.6875, 0.359375, 0.6875, 0.359375, 0.6875]","[0.16836410271566996, 0.1268899293073012, 0.05000000074505806, 0.2266202065030058, 0.26601246986512583, 0.09606165575313837, 0.3887089771294086, 0.05000000074505806, 0.10507199902117154, 0.10102239109829335, 0.536285617541524, 0.2520626323882154, 0.05000000074505806, 0.2512144069876864, 0.305286795688664, 0.05000000074505806, 0.43430860485763195, 0.05765624593876015, 0.05000000074505806]"
-230,33.15,0.33149999999999996,20282688,50.018465909090914,28.47969338353954,BEST_adc_episode_230,"[0.125, 0.0625, 0.125, 0.0625, 0.25, 0.125, 0.0625, 0.03125, 0.3125, 0.03125, 0.46875, 0.625, 0.46875, 0.65625, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375]","[0.1678187929970466, 0.12119400239320365, 0.16845840952645902, 0.10827507758673577, 0.278668688139908, 0.15169075297165405, 0.08435086799477534, 0.05000000074505806, 0.33251709704772353, 0.05412856166594115, 0.4120265979115352, 0.055021633637852015, 0.05000000074505806, 0.14166582813856343, 0.1442506598782534, 0.05955852337911669, 0.07280424091566871, 0.1486065767262081, 0.18518294671746272]"
-231,33.81999999999999,0.33819999999999995,20280384,50.01278409090909,29.495024206562668,BEST_adc_episode_231,"[0.4375, 0.4375, 0.0625, 0.0625, 0.0625, 0.0625, 0.125, 0.03125, 0.09375, 0.25, 0.15625, 0.65625, 0.4375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875, 0.390625]","[0.4626687210703707, 0.45745875424649407, 0.06871420990734711, 0.12030595106828543, 0.06378655927584538, 0.09347728984716724, 0.16593358787662243, 0.05000000074505806, 0.10868762067379235, 0.2646650482233018, 0.16273956391738642, 0.46667450196737176, 0.05000000074505806, 0.32099630882842434, 0.3385411946638468, 0.1526534656576592, 0.1625558423801459, 0.07155240777489275, 0.18914912470449818]"
-232,35.760000000000005,0.35760000000000003,20281536,50.015625,25.245427649273804,BEST_adc_episode_232,"[0, 0, 0.0625, 0, 0, 0, 0.0625, 0.03125, 0.75, 0.1875, 0.75, 0.21875, 0.71875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05667148445094903, 0.05000000074505806, 0.0670990166019552, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06262257353844045, 0.05000000074505806, 0.05000000074505806, 0.0675258525544597, 0.05000000074505806, 0.09774022900653331, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-233,47.0,0.47,20284416,50.02272727272727,28.85287789133943,BEST_adc_episode_233,"[0.375, 0.125, 0.125, 0.125, 0.125, 0.0625, 0.0625, 0.1875, 0.375, 0.09375, 0.15625, 0.5, 0.59375, 0.5625, 0.53125, 0.578125, 0.53125, 0.5625, 0.546875]","[0.38364874240618074, 0.1438950400152775, 0.1708267197122324, 0.18108311261257334, 0.15421371298116388, 0.08147300111964663, 0.10026594716417292, 0.20526650755512418, 0.3857663973969828, 0.0994973350607748, 0.1699731876010367, 0.23740029797802545, 0.12561443984358192, 0.27951180816101534, 0.05000000074505806, 0.13626552275597098, 0.1963914946278907, 0.06144189611315606, 0.3477730132100145]"
-234,36.46,0.36460000000000004,20276928,50.00426136363636,28.698224852071007,BEST_adc_episode_234,"[0.0625, 0.0625, 0.125, 0.3125, 0.125, 0, 0.25, 0.03125, 0.1875, 0.03125, 0.53125, 0.5625, 0.53125, 0.609375, 0.484375, 0.609375, 0.5, 0.59375, 0.515625]","[0.08131693119421385, 0.07380046968710485, 0.16298708750514607, 0.315902929913257, 0.1280199764365256, 0.05000000074505806, 0.25194771777887, 0.05000000074505806, 0.20335331222305153, 0.06025650696870496, 0.13577805193766865, 0.14214831092636274, 0.20339035836469543, 0.23381120175516873, 0.08838134591453278, 0.05000000074505806, 0.2140421960596451, 0.28808780636843434, 0.2594499501465321]"
-235,36.99,0.3699,20279232,50.00994318181819,27.98547606239914,BEST_adc_episode_235,"[0.0625, 0.25, 0.0625, 0, 0, 0.125, 0.3125, 0.1875, 0.15625, 0.0625, 0.625, 0.46875, 0.625, 0.5, 0.59375, 0.515625, 0.578125, 0.53125, 0.578125]","[0.06883841438639905, 0.30163586425423616, 0.06957135335927475, 0.05000000074505806, 0.05000000074505806, 0.16661471762038912, 0.3324418271846681, 0.21135226896083753, 0.16862936758367472, 0.08567108627480827, 0.05000000074505806, 0.05000000074505806, 0.0590008939209854, 0.10805021755781899, 0.19605973911047153, 0.09664693058569768, 0.05000000074505806, 0.05566717320491887, 0.12545560198230538]"
-236,46.19,0.4619,20276928,50.00426136363636,27.35677783754707,BEST_adc_episode_236,"[0.0625, 0.125, 0.0625, 0, 0.1875, 0.0625, 0, 0.03125, 0.09375, 0.6875, 0.40625, 0.65625, 0.4375, 0.640625, 0.4375, 0.640625, 0.453125, 0.625, 0.484375]","[0.09595323478194949, 0.16500381178613538, 0.11676658300258137, 0.05000000074505806, 0.22843298473962168, 0.07995973493327643, 0.05000000074505806, 0.05000000074505806, 0.09494910378598272, 0.16418602000486776, 0.05000000074505806, 0.3594062857719495, 0.16529622283471687, 0.11160601538485572, 0.13100010558398004, 0.05000000074505806, 0.05000000074505806, 0.16125512737900435, 0.05000000074505806]"
-237,40.56999999999999,0.40569999999999995,20286720,50.028409090909086,27.655997848305542,BEST_adc_episode_237,"[0.125, 0, 0.0625, 0.125, 0.25, 0, 0.0625, 0.0625, 0.09375, 0.46875, 0.65625, 0.40625, 0.65625, 0.453125, 0.625, 0.484375, 0.609375, 0.484375, 0.609375]","[0.13263794013572253, 0.05000000074505806, 0.10031607605153163, 0.1602023949110839, 0.2677917456983866, 0.05000000074505806, 0.09596342053454478, 0.08353448020640071, 0.11312275209361125, 0.05000000074505806, 0.060120900544205436, 0.10391417913115403, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.18339058791686594, 0.05000000074505806, 0.24185779892069698, 0.11379554585043154]"
-238,29.310000000000002,0.2931,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_238,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06275335500321319, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.051621943420105586, 0.05000000074505806, 0.05475580639203244, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-239,37.870000000000005,0.37870000000000004,20275776,50.00142045454545,27.017213555675095,BEST_adc_episode_239,"[0, 0.0625, 0.25, 0, 0, 0.0625, 0.0625, 0.0625, 0.0625, 0.78125, 0.1875, 0.75, 0.21875, 0.734375, 0.25, 0.734375, 0.25, 0.734375, 0.25]","[0.05000000074505806, 0.11320360831876729, 0.27937701935000114, 0.05000000074505806, 0.05000000074505806, 0.09491651901101289, 0.07454993619151067, 0.08831343091324191, 0.05000000074505806, 0.05000000074505806, 0.05136387827869862, 0.05000000074505806, 0.05000000074505806, 0.08921677888946394, 0.05000000074505806, 0.11116264231309264, 0.1825879341414115, 0.18374287230318342, 0.06578534950460935]"
-240,34.78,0.3478,20275200,50.0,26.802044109736418,BEST_adc_episode_240,"[0, 0, 0, 0.125, 0.125, 0.0625, 0.125, 0.09375, 0.0625, 0.78125, 0.1875, 0.75, 0.21875, 0.75, 0.1875, 0.75, 0.21875, 0.734375, 0.28125]","[0.05000000074505806, 0.05000000074505806, 0.058599213614465316, 0.18017955676708353, 0.14186880192514714, 0.09067044704484366, 0.15304911306716437, 0.10239689241175642, 0.05000000074505806, 0.18207887268771367, 0.0936345896524671, 0.09814630808234555, 0.10166854890786343, 0.0758645589069319, 0.08312818423896882, 0.14264875776272604, 0.07568379923321489, 0.0907544560349432, 0.05000000074505806]"
-241,41.86,0.41859999999999997,20275776,50.00142045454545,26.01869284561592,BEST_adc_episode_241,"[0, 0, 0, 0.1875, 0, 0, 0.0625, 0.09375, 0.40625, 0.6875, 0.375, 0.65625, 0.4375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875, 0.390625]","[0.05000000074505806, 0.05000000074505806, 0.05432660978203682, 0.21783699897090808, 0.05000000074505806, 0.05000000074505806, 0.07205796027763044, 0.09509276433451046, 0.12686852022099648, 0.15646774660770735, 0.10510429927675098, 0.06552192305602042, 0.05000000074505806, 0.05365448425379649, 0.11001232173865133, 0.08350990963850144, 0.05000000074505806, 0.08608195144494878, 0.13772177416794795]"
-242,35.84,0.35840000000000005,20290176,50.03693181818182,25.830419580419576,BEST_adc_episode_242,"[0.125, 0, 0, 0, 0.0625, 0, 0.0625, 0.0625, 0.5625, 0.53125, 0.59375, 0.5, 0.59375, 0.546875, 0.546875, 0.5625, 0.546875, 0.5625, 0.53125]","[0.16421727189596122, 0.05000000074505806, 0.05453321907587991, 0.05000000074505806, 0.09311441298819888, 0.05000000074505806, 0.07214684157056064, 0.06860389231599619, 0.07899126098965681, 0.05000000074505806, 0.12548274166292767, 0.06617469958736705, 0.05000000074505806, 0.12904529926069908, 0.18630640862712683, 0.0676174616300893, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-243,20.85,0.20850000000000002,20277504,50.00568181818181,25.92791823561054,BEST_adc_episode_243,"[0.125, 0.0625, 0.0625, 0, 0, 0, 0, 0.03125, 0.5625, 0.5625, 0.53125, 0.59375, 0.5, 0.609375, 0.484375, 0.609375, 0.5, 0.59375, 0.515625]","[0.12664813245564568, 0.11408190078437364, 0.08854513672975818, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09613228416660839, 0.05000000074505806, 0.05000000074505806, 0.05136829209808042, 0.05000000074505806, 0.08437539751928287, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-244,33.040000000000006,0.3304000000000001,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_244,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05124300277644799, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-245,49.660000000000004,0.49660000000000004,20289024,50.034090909090914,27.5080688542227,BEST_adc_episode_245,"[0, 0.0625, 0.125, 0.0625, 0.125, 0.0625, 0.1875, 0.09375, 0.0625, 0.4375, 0.65625, 0.4375, 0.625, 0.546875, 0.5625, 0.53125, 0.578125, 0.53125, 0.5625]","[0.05000000074505806, 0.09601502502447486, 0.13925340341777054, 0.06426320053330217, 0.1414931123265857, 0.09171701394396614, 0.24001549524750648, 0.10263521712761448, 0.08436346847736731, 0.05000000074505806, 0.06463157284826379, 0.07975302369149664, 0.05000000074505806, 0.05000000074505806, 0.055100488538604886, 0.05000000074505806, 0.05000000074505806, 0.06557650856747388, 0.08484217413231239]"
-246,28.15,0.2815,20279808,50.01136363636364,25.245427649273804,BEST_adc_episode_246,"[0.0625, 0, 0, 0, 0, 0, 0.0625, 0.03125, 0.78125, 0.1875, 0.75, 0.1875, 0.75, 0.28125, 0.71875, 0.28125, 0.71875, 0.296875, 0.71875]","[0.06948636521877204, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05965848618901586, 0.09434940408039301, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.15463406379314049, 0.11569336936132471, 0.16445339379889182, 0.08989172646353649, 0.05000000074505806, 0.14983527268424893, 0.06742796443553652, 0.05000000074505806]"
-247,38.480000000000004,0.38480000000000003,20277504,50.00568181818181,26.0691231845078,BEST_adc_episode_247,"[0.0625, 0.0625, 0, 0.0625, 0, 0, 0.125, 0.125, 0.34375, 0.6875, 0.375, 0.6875, 0.34375, 0.703125, 0.328125, 0.703125, 0.328125, 0.703125, 0.328125]","[0.08714125348834471, 0.0749003807339967, 0.05000000074505806, 0.10874709333690075, 0.05000000074505806, 0.05000000074505806, 0.1260052653632942, 0.13405024498319876, 0.05920051810630688, 0.05000000074505806, 0.05364631758249247, 0.05000000074505806, 0.07914600494973018, 0.05000000074505806, 0.05000000074505806, 0.07243769116754545, 0.05002276241243761, 0.09223823305510262, 0.1060163029355995]"
-248,36.57,0.3657,20281536,50.015625,25.245427649273804,BEST_adc_episode_248,"[0, 0, 0, 0.0625, 0, 0, 0.0625, 0.03125, 0.75, 0.1875, 0.75, 0.21875, 0.71875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08357753361101038, 0.05000000074505806, 0.05000000074505806, 0.09902679975543875, 0.05000000074505806, 0.05000000074505806, 0.07686357137917488, 0.05000000074505806, 0.167104074291189, 0.05005063668649833, 0.1165143725951075, 0.05000000074505806, 0.05205403317174303, 0.1118524483499588, 0.05000000074505806, 0.05000000074505806]"
-249,41.2,0.41200000000000003,20285568,50.02556818181818,25.985072619688005,BEST_adc_episode_249,"[0, 0, 0.125, 0, 0.0625, 0, 0, 0.03125, 0.5625, 0.59375, 0.5, 0.59375, 0.53125, 0.578125, 0.515625, 0.59375, 0.5, 0.59375, 0.515625]","[0.05000000074505806, 0.05000000074505806, 0.1605292199446924, 0.05000000074505806, 0.08411390582118221, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.11475321783027413, 0.052128296376446975, 0.05000000074505806, 0.05000000074505806, 0.06691132595387637, 0.05000000074505806, 0.07187710979689037, 0.09128296869138823, 0.05000000074505806, 0.05000000074505806]"
-250,28.01,0.2801,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_250,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05783479541553726, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09031366263396501, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07047469932014369]"
-251,31.46,0.3146,20284992,50.02414772727273,25.51775147928994,BEST_adc_episode_251,"[0.0625, 0, 0, 0, 0, 0.0625, 0, 0.125, 0.6875, 0.375, 0.65625, 0.4375, 0.65625, 0.421875, 0.65625, 0.40625, 0.65625, 0.4375, 0.640625]","[0.10992903646645212, 0.05000000074505806, 0.0511179725475629, 0.05000000074505806, 0.05096498760888819, 0.08996691724304642, 0.05000000074505806, 0.1314098012227564, 0.05000000074505806, 0.05000000074505806, 0.07023691083984056, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06299318493161887, 0.05000000074505806, 0.05000000074505806]"
-252,27.149999999999995,0.27149999999999996,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_252,"[0, 0, 0, 0.0625, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.05000000074505806, 0.054180314594610784, 0.08240647211066485, 0.05000000074505806, 0.05947523826549712, 0.05000000074505806, 0.05000000074505806, 0.0667592240220295, 0.08229344764716919, 0.05000000074505806, 0.05000000074505806, 0.053180000314055086, 0.08881519324368006, 0.05000000074505806, 0.05000000074505806, 0.1269814947921739, 0.05000000074505806, 0.05000000074505806]"
-253,42.010000000000005,0.42010000000000003,20276352,50.00284090909091,25.134480903711676,BEST_adc_episode_253,"[0, 0, 0, 0, 0, 0, 0.0625, 0.1875, 0.75, 0.25, 0.71875, 0.3125, 0.71875, 0.28125, 0.71875, 0.296875, 0.703125, 0.359375, 0.6875]","[0.052846681709082445, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07799602010589665, 0.15429799836910205, 0.05000000074505806, 0.05000000074505806, 0.05959896660142294, 0.05000000074505806, 0.09111483826973861, 0.05000000074505806, 0.05936969285102976, 0.06184500533827986, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-254,35.519999999999996,0.35519999999999996,20281536,50.015625,25.245427649273804,BEST_adc_episode_254,"[0, 0.0625, 0, 0, 0, 0, 0.0625, 0.03125, 0.75, 0.1875, 0.75, 0.21875, 0.71875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.07615854772392312, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08675525361970375, 0.05290686855833323, 0.05159467698574209, 0.060041903167176745, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09119425998942189, 0.06831184828719833, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09715208817360843]"
-255,34.58,0.3458,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_255,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.054930462885116084, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06618215798231294, 0.05000000074505806, 0.05000000074505806, 0.05070127243586599, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-256,27.080000000000005,0.27080000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_256,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.058633852321794036, 0.053322571650782496, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.057798106007041834, 0.05000000074505806, 0.0679040326845646, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05875365343794243, 0.05000000074505806, 0.05000000074505806]"
-257,29.990000000000006,0.29990000000000006,20289024,50.034090909090914,25.601802044109732,BEST_adc_episode_257,"[0, 0, 0.0625, 0, 0.0625, 0, 0, 0.03125, 0.71875, 0.28125, 0.71875, 0.28125, 0.71875, 0.34375, 0.6875, 0.375, 0.671875, 0.40625, 0.65625]","[0.05000000074505806, 0.05000000074505806, 0.07716222015975503, 0.05000000074505806, 0.06372866255417356, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06323571488871862, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-258,36.36000000000001,0.3636000000000001,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_258,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05355460203296024, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.057921293797342885, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07186001194650404, 0.05000000074505806, 0.05000000074505806]"
-259,27.180000000000003,0.27180000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_259,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0729535788868925, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-260,36.77,0.3677,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_260,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-261,23.640000000000004,0.23640000000000005,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_261,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-262,30.259999999999998,0.3026,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_262,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.056995225891986925, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08006233969568348, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07102798788722746, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-263,27.54,0.2754,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_263,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-264,32.02,0.32020000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_264,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-265,36.88,0.3688,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_265,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-266,36.18,0.3618,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_266,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-267,24.11,0.24109999999999998,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_267,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-268,35.22,0.3522,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_268,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05392456798219033, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.09308605384517991, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-269,32.42,0.32420000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_269,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0509329188522249, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-270,37.150000000000006,0.37150000000000005,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_270,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-271,24.129999999999995,0.24129999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_271,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-272,26.980000000000004,0.26980000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_272,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-273,26.049999999999997,0.26049999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_273,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-274,28.139999999999997,0.2814,20275776,50.00142045454545,25.79679935449166,BEST_adc_episode_274,"[0, 0, 0.0625, 0, 0.0625, 0.0625, 0, 0.03125, 0.59375, 0.5, 0.59375, 0.5, 0.59375, 0.5625, 0.546875, 0.546875, 0.5625, 0.546875, 0.5625]","[0.05000000074505806, 0.05000000074505806, 0.06354324001035562, 0.05000000074505806, 0.06529755446581373, 0.07525075877805687, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0598721466831172, 0.05000000074505806, 0.05000000074505806, 0.07511612140545657, 0.07358062596697869, 0.05000000074505806, 0.1036600339201934, 0.05000000074505806, 0.05000000074505806]"
-275,39.26,0.3926,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_275,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-276,37.1,0.371,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_276,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-277,38.07000000000001,0.3807000000000001,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_277,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-278,28.15,0.2815,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_278,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-279,34.28,0.3428,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_279,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-280,36.029999999999994,0.36029999999999995,20288448,50.03267045454546,25.134480903711676,BEST_adc_episode_280,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.75, 0.21875, 0.71875, 0.34375, 0.6875, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375, 0.640625]","[0.06309994139485617, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08494825760382697, 0.05000000074505806, 0.05000000074505806]"
-281,29.869999999999997,0.29869999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_281,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-282,30.110000000000003,0.30110000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_282,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-283,38.029999999999994,0.3802999999999999,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_283,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-284,27.569999999999993,0.27569999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_284,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-285,32.68,0.3268,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_285,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-286,27.139999999999997,0.2714,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_286,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06059216000531128, 0.05000000074505806, 0.053436054689906864, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06843313038430326, 0.05000000074505806, 0.05000000074505806, 0.050805925476699626, 0.05000000074505806, 0.05000000074505806, 0.05210079398843587, 0.05000000074505806, 0.05000000074505806]"
-287,35.85,0.35850000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_287,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-288,35.06,0.3506,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_288,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-289,27.02,0.2702,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_289,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-290,21.230000000000004,0.21230000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_290,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-291,31.279999999999998,0.31279999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_291,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-292,31.979999999999997,0.3198,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_292,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.053183060106826094, 0.05000000074505806, 0.050310522919805534, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-293,34.27,0.3427,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_293,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-294,30.700000000000006,0.30700000000000005,19642752,48.44034090909091,21.254706831629907,BEST_adc_episode_294,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.765625, 0.59375, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.051550575331630856, 0.05000000074505806, 0.1568417057802262, 0.0900451862484807, 0.3288652803483459, 0.05000000074505806, 0.5667374957136668, 0.3726911365919003, 0.7812320360106371, 0.5957901895952499, 0.789058778376256]"
-295,19.77,0.1977,13296960,32.79119318181819,9.037116729424422,BEST_adc_episode_295,"[0, 0.1875, 0.125, 0.125, 0.125, 0.0625, 0.0625, 0.65625, 0.78125, 0.75, 0.78125, 0.75, 0.75, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.06059230452987132, 0.1940969844592031, 0.16263225468965362, 0.17080007372095085, 0.15046990271633517, 0.1157101565716005, 0.10926027304213318, 0.6774735968097788, 0.7935045947531008, 0.7735208214227399, 0.7819028379588234, 0.7757849036791266, 0.7790314077505105, 0.7981465212724415, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7988371926256703, 0.800000011920929]"
-296,18.600000000000005,0.18600000000000005,16870464,41.60369318181818,11.135018827326519,BEST_adc_episode_296,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.71875, 0.75, 0.71875, 0.75, 0.796875, 0.796875, 0.796875, 0.796875, 0.78125, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.10078082526255225, 0.7960722399834933, 0.7339079625694664, 0.7531776728620626, 0.7358359317458885, 0.7502280209022879, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.796839091743151, 0.7952265199677119]"
-297,15.780000000000005,0.15780000000000005,16847424,41.546875,11.000537923614846,BEST_adc_episode_297,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.71875, 0.75, 0.71875, 0.75, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7884608822311061, 0.7320105934456346, 0.7609670287672506, 0.7471397328027086, 0.7553894958647509, 0.7995971217297175, 0.7988138640613683, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7995363197738642]"
-298,15.229999999999999,0.1523,16835328,41.51704545454545,11.081226465841851,BEST_adc_episode_298,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.75, 0.75, 0.75, 0.6875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.78125]","[0.055211397983862584, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05605969824094913, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.770675631549369, 0.7555385846828568, 0.7513174705500355, 0.7076377724543315, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.800000011920929, 0.7864324401077845]"
-299,17.259999999999998,0.17259999999999998,16805952,41.44460227272727,11.071140398063475,BEST_adc_episode_299,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.75, 0.75, 0.75, 0.75, 0.796875, 0.78125, 0.796875, 0.796875, 0.78125, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7981815690468282, 0.755515987814352, 0.7720144702521504, 0.7688361595141106, 0.7635488581473379, 0.800000011920929, 0.7960726602459142, 0.800000011920929, 0.800000011920929, 0.7963784259198401, 0.7971237872262427]"
-300,22.250000000000004,0.22250000000000003,16776000,41.37073863636363,10.8963152232383,BEST_adc_episode_300,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.75, 0.75, 0.75, 0.75, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7981081171133637, 0.7572797222396342, 0.7659723119416851, 0.7617062522990833, 0.7629453541134603, 0.800000011920929, 0.7992495571179616, 0.7999914270358364, 0.7999194334313693, 0.7971840877590044, 0.800000011920929]"
-301,17.75,0.1775,16890624,41.653409090909086,11.161915008068855,BEST_adc_episode_301,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.71875, 0.75, 0.71875, 0.71875, 0.796875, 0.796875, 0.78125, 0.796875, 0.796875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7928278326221119, 0.7227813382865627, 0.7521926231011723, 0.7266249801606518, 0.7444780850090512, 0.800000011920929, 0.800000011920929, 0.7967866151026266, 0.800000011920929, 0.800000011920929, 0.800000011920929]"
-302,24.28,0.24280000000000002,17239104,42.512784090909086,11.703200645508339,BEST_adc_episode_302,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.59375, 0.71875, 0.625, 0.6875, 0.796875, 0.796875, 0.78125, 0.796875, 0.796875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7857102206535946, 0.6047194991977847, 0.7362356393220129, 0.6277589776166674, 0.6945284007113155, 0.7980002816464855, 0.799108000981067, 0.7965304456970493, 0.800000011920929, 0.7990799738189661, 0.800000011920929]"
-303,21.940000000000005,0.21940000000000004,18802944,46.36931818181818,16.35287789133943,BEST_adc_episode_303,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.734375, 0.765625, 0.734375, 0.734375]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.5708759569834408, 0.1031088374737956, 0.5502010907530744, 0.06637936622009809, 0.386749300390444, 0.2875725555052169, 0.7998308762803633, 0.7409007214810035, 0.772751793419096, 0.7343883986986655, 0.7493960830564278]"
-304,30.010000000000005,0.30010000000000003,19070784,47.02982954545455,17.916218396987627,BEST_adc_episode_304,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.75, 0.6875, 0.703125, 0.703125, 0.75]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.10145037832393268, 0.05000000074505806, 0.23915591589655816, 0.05000000074505806, 0.11664283627884346, 0.11765080676624355, 0.7606020281940092, 0.6902827264187266, 0.7104940717902184, 0.7045219143324264, 0.7603754810427233]"
-305,29.979999999999997,0.29979999999999996,19548288,48.20738636363636,20.70333512641205,BEST_adc_episode_305,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.34375, 0.796875, 0.265625, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05505067543349059, 0.05000000074505806, 0.07671012040949504, 0.05000000074505806, 0.05149271786313044, 0.05000000074505806, 0.7991480296407916, 0.34651068522762046, 0.800000011920929, 0.27839184578194, 0.800000011920929]"
-306,33.14,0.33140000000000003,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_306,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7679411741398575, 0.05000000074505806, 0.7997552737526441, 0.05000000074505806, 0.7978242634925741]"
-307,34.57,0.3457,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_307,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7782423885054678, 0.05000000074505806, 0.7986080801365256, 0.05000000074505806, 0.800000011920929]"
-308,37.33,0.37329999999999997,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_308,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.774507479762928, 0.05000000074505806, 0.798600904876897, 0.05000000074505806, 0.800000011920929]"
-309,37.67,0.37670000000000003,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_309,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7723804369298286, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-310,29.93,0.2993,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_310,"[0, 0, 0, 0.0625, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.050538544422263215, 0.05000000074505806, 0.05000000074505806, 0.07018124087327134, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.11472396448476274, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06996957382713205, 0.05038259458041711, 0.05000000074505806, 0.4972269552238258, 0.07794228678196913, 0.41566152650436566, 0.05000000074505806, 0.4325925868203505]"
-311,27.900000000000002,0.279,20158848,49.71306818181818,24.267079074771384,BEST_adc_episode_311,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.765625, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7738665412532315, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-312,36.519999999999996,0.36519999999999997,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_312,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7845300041764209, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7982069581327716]"
-313,34.84,0.34840000000000004,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_313,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7958214030061295, 0.05000000074505806, 0.7996068155311341, 0.05000000074505806, 0.800000011920929]"
-314,39.1,0.391,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_314,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7868070203937451, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-315,35.540000000000006,0.35540000000000005,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_315,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.798826569087774, 0.05000000074505806, 0.7987096188194645, 0.05000000074505806, 0.7998289157989188]"
-316,29.979999999999997,0.29979999999999996,20262528,49.96875,25.063878429263042,BEST_adc_episode_316,"[0, 0, 0.0625, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.734375, 0.046875, 0.796875, 0.046875, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.08798688782260276, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0600008567261778, 0.10865915426407939, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7477699657796414, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7817461210378533]"
-317,30.589999999999996,0.30589999999999995,20206080,49.82954545454545,24.54276492738031,BEST_adc_episode_317,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.734375, 0.109375, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7375852223442056, 0.05000000074505806, 0.7997728713767578, 0.05000000074505806, 0.7998608990290516]"
-318,36.760000000000005,0.36760000000000004,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_318,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7986159244201252, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7997871101434377]"
-319,30.989999999999995,0.30989999999999995,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_319,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7959525550861958, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-320,37.28,0.3728,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_320,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7992763518918721, 0.05000000074505806, 0.7993675270179538]"
-321,36.35,0.3635,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_321,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7969028453892191, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-322,33.07,0.3307,20218176,49.859375,24.61336740182894,BEST_adc_episode_322,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.765625, 0.046875, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05881605674061782, 0.05000000074505806, 0.05000000074505806, 0.05187649802212121, 0.056655523476377256, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0776438087865993, 0.800000011920929, 0.05000000074505806, 0.7707564175561004, 0.05000000074505806, 0.7917296183670047]"
-323,37.129999999999995,0.37129999999999996,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_323,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7996226501861178, 0.05000000074505806, 0.7997475530182977, 0.05000000074505806, 0.800000011920929]"
-324,37.03999999999999,0.3703999999999999,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_324,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7981804054992995, 0.05000000074505806, 0.7997328785553669, 0.05000000074505806, 0.800000011920929]"
-325,35.370000000000005,0.35370000000000007,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_325,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7998031809128667, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7997147323405739]"
-326,30.300000000000004,0.30300000000000005,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_326,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7816368481610052, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7995447346126654]"
-327,38.88,0.38880000000000003,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_327,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7875837892561417, 0.05000000074505806, 0.7994421257520891, 0.05000000074505806, 0.800000011920929]"
-328,20.340000000000003,0.20340000000000003,20280960,50.01420454545455,25.17146315223238,BEST_adc_episode_328,"[0, 0.0625, 0, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.625, 0.46875, 0.625, 0.46875, 0.625]","[0.05000000074505806, 0.06690182303015572, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0630313660548274, 0.05355372314488726, 0.08531092750855038, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.496434918376349, 0.05000000074505806, 0.3761992687194966, 0.07555022201305503, 0.3822378644693491]"
-329,35.5,0.355,20100672,49.56960227272727,23.92751479289941,BEST_adc_episode_329,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7932763198527022, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-330,27.400000000000002,0.274,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_330,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.1262007363787338, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-331,38.7,0.387,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_331,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-332,35.62,0.35619999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_332,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-333,30.630000000000003,0.3063,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_333,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-334,29.21,0.2921,20290176,50.03693181818182,25.36646046261431,BEST_adc_episode_334,"[0.0625, 0.0625, 0, 0, 0, 0, 0, 0.09375, 0.71875, 0.28125, 0.71875, 0.3125, 0.6875, 0.46875, 0.625, 0.46875, 0.609375, 0.5, 0.59375]","[0.08387220180453099, 0.08845159713811707, 0.05000000074505806, 0.05000000074505806, 0.06074490286477618, 0.05000000074505806, 0.05000000074505806, 0.10563286657446672, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06379387717517108, 0.05000000074505806, 0.06124240406702987, 0.05000000074505806, 0.05000000074505806, 0.06407003224428641, 0.05000000074505806, 0.05000000074505806]"
-335,36.6,0.366,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_335,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-336,37.739999999999995,0.37739999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_336,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-337,34.709999999999994,0.34709999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_337,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-338,37.59,0.3759,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_338,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-339,33.9,0.33899999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_339,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-340,16.469999999999995,0.16469999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_340,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05745587104699142, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0567548266812444, 0.05000000074505806, 0.05000000074505806, 0.05275428790053619, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-341,26.990000000000002,0.26990000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_341,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-342,26.419999999999998,0.2642,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_342,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-343,32.599999999999994,0.32599999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_343,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-344,33.03,0.33030000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_344,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-345,38.0,0.38,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_345,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-346,25.670000000000005,0.25670000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_346,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06467178421140847, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-347,35.150000000000006,0.35150000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_347,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-348,34.46,0.3446,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_348,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-349,37.8,0.37799999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_349,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-350,33.599999999999994,0.33599999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_350,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-351,33.13,0.33130000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_351,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-352,39.559999999999995,0.39559999999999995,20288448,50.03267045454546,25.134480903711676,BEST_adc_episode_352,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.75, 0.21875, 0.71875, 0.34375, 0.6875, 0.421875, 0.640625, 0.453125, 0.640625, 0.4375, 0.640625]","[0.06764028558521103, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06944207335323059, 0.05000000074505806, 0.05980479777282769, 0.05000000074505806, 0.05000000074505806, 0.08278919461090165, 0.05000000074505806, 0.05000000074505806, 0.06693517116261877, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-353,34.650000000000006,0.34650000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_353,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-354,35.39,0.3539,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_354,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-355,39.94,0.3994,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_355,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-356,39.410000000000004,0.39410000000000006,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_356,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-357,41.86,0.41859999999999997,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_357,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-358,42.669999999999995,0.42669999999999997,20277504,50.00568181818181,26.089295320064547,BEST_adc_episode_358,"[0.125, 0, 0, 0, 0.0625, 0.0625, 0.0625, 0.03125, 0.46875, 0.625, 0.46875, 0.625, 0.46875, 0.640625, 0.4375, 0.640625, 0.453125, 0.625, 0.484375]","[0.13781693189587144, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0916061531844739, 0.0748224717723815, 0.08170281463054967, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07149291293574199, 0.05000000074505806, 0.05919709701568539, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08669890644007629, 0.05000000074505806, 0.05000000074505806]"
-359,35.480000000000004,0.35480000000000006,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_359,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-360,36.35,0.3635,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_360,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-361,32.97,0.3297,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_361,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-362,32.03,0.32030000000000003,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_362,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-363,27.769999999999996,0.27769999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_363,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-364,27.569999999999993,0.27569999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_364,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06604984678704297, 0.05000000074505806, 0.050516675251807044, 0.05000000074505806, 0.07346507081518264, 0.05000000074505806, 0.05000000074505806, 0.08434014389312787, 0.05510932642411254, 0.05000000074505806, 0.05000000074505806]"
-365,26.61,0.2661,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_365,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-366,26.490000000000002,0.2649,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_366,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-367,32.8,0.32799999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_367,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-368,36.63999999999999,0.36639999999999995,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_368,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-369,30.95,0.3095,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_369,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-370,37.39,0.3739,20276352,50.00284090909091,25.134480903711676,BEST_adc_episode_370,"[0, 0, 0, 0, 0, 0, 0.0625, 0.1875, 0.75, 0.25, 0.71875, 0.3125, 0.71875, 0.28125, 0.71875, 0.296875, 0.703125, 0.359375, 0.6875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07724701023250888, 0.05000000074505806, 0.10838769505330081, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.054880009844897125, 0.09657075409994408, 0.05000000074505806, 0.05000000074505806]"
-371,37.88,0.3788,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_371,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-372,30.000000000000004,0.30000000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_372,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-373,31.55,0.3155,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_373,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-374,38.42,0.38420000000000004,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_374,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-375,36.83,0.36829999999999996,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_375,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7999160048487227, 0.05000000074505806, 0.7999776520524081, 0.05000000074505806, 0.7999982880891966]"
-376,41.82,0.4182,20287296,50.02982954545454,25.01681011296396,BEST_adc_episode_376,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.75, 0.046875, 0.796875, 0.046875, 0.765625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07504612333794451, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7609988957327879, 0.05000000074505806, 0.800000011920929, 0.05052241054956185, 0.7481229893569016]"
-377,30.23,0.3023,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_377,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7984202486918578, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999904780993354]"
-378,38.580000000000005,0.38580000000000003,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_378,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7979975922396392, 0.05000000074505806, 0.7999848263259557, 0.05000000074505806, 0.800000011920929]"
-379,32.04,0.3204,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_379,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7985049887722581, 0.05000000074505806, 0.7999961275556107, 0.05000000074505806, 0.800000011920929]"
-380,34.81999999999999,0.34819999999999995,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_380,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.79987448199003, 0.05000000074505806, 0.7999936451797248, 0.05000000074505806, 0.7999782465466962]"
-381,34.43000000000001,0.34430000000000005,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_381,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7998357648980032, 0.05000000074505806, 0.799981976403989, 0.05000000074505806, 0.800000011920929]"
-382,35.650000000000006,0.35650000000000004,20207232,49.83238636363637,24.74112426035503,BEST_adc_episode_382,"[0, 0, 0, 0.0625, 0, 0, 0, 0.125, 0.75, 0.28125, 0.71875, 0.3125, 0.6875, 0.453125, 0.75, 0.046875, 0.796875, 0.046875, 0.78125]","[0.05000000074505806, 0.05614451415140548, 0.05000000074505806, 0.10837621709892249, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06434909528670178, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05741858769635604, 0.7594589860503417, 0.05000000074505806, 0.800000011920929, 0.060831954260901315, 0.791398980091768]"
-383,36.41,0.3641,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_383,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7998768180618998, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999954201080673]"
-384,41.02,0.4102,20042496,49.42613636363637,23.587950511027433,BEST_adc_episode_384,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.79996172009316, 0.05000000074505806, 0.7999995120786205, 0.05000000074505806, 0.7999852914113161]"
-385,32.66,0.32659999999999995,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_385,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7602598121356307, 0.05000000074505806, 0.7999539196643276, 0.05000000074505806, 0.7998746529189187, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999948055517522, 0.05000000074505806, 0.7999889948797158]"
-386,27.280000000000005,0.27280000000000004,20201472,49.81818181818182,24.57638515330823,BEST_adc_episode_386,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.71875, 0.21875, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7350039523816767, 0.05000000074505806, 0.7989074659201285, 0.05000000074505806, 0.7999793420953429, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999953121574408]"
-387,32.410000000000004,0.32410000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_387,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7928503464773604, 0.05000000074505806, 0.7999888620492813, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999863352510068, 0.05000000074505806, 0.799997155652632, 0.05000000074505806, 0.800000011920929]"
-388,25.670000000000005,0.25670000000000004,20276928,50.00426136363636,25.793437331898872,BEST_adc_episode_388,"[0, 0, 0.0625, 0, 0.0625, 0, 0, 0.03125, 0.71875, 0.28125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.78125, 0.046875, 0.78125]","[0.05000000074505806, 0.05000000074505806, 0.06289767189719724, 0.05458507088827692, 0.07541382405120237, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7400022947475926, 0.05777602716586765, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7886471828773967, 0.05000000074505806, 0.7701804775906668]"
-389,31.820000000000004,0.31820000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_389,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.790671088221282, 0.05000000074505806, 0.7999973954496711, 0.05000000074505806, 0.7999885101600638, 0.05000000074505806, 0.7999953940722239, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-390,32.52,0.32520000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_390,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7920494428990174, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999859012142811, 0.05000000074505806, 0.7999957066361391, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-391,31.710000000000004,0.31710000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_391,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7903354248920224, 0.05000000074505806, 0.7999808409872877, 0.05000000074505806, 0.7999882001461434, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-392,33.330000000000005,0.33330000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_392,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7893398036244154, 0.05000000074505806, 0.799991994898789, 0.05000000074505806, 0.799995204395773, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999999163692463]"
-393,29.720000000000002,0.2972,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_393,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7924548328543447, 0.05000000074505806, 0.7999958121339986, 0.05000000074505806, 0.7999858480720082, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999943372594015, 0.05000000074505806, 0.800000011920929]"
-394,34.230000000000004,0.34230000000000005,20155392,49.70454545454545,24.74112426035503,BEST_adc_episode_394,"[0, 0, 0.0625, 0, 0, 0, 0, 0.125, 0.75, 0.28125, 0.78125, 0.03125, 0.78125, 0.046875, 0.765625, 0.203125, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.08691364017762691, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.08968459011250728, 0.776630368427483, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7932091931740264, 0.05000000074505806, 0.7535585073059262, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-395,32.65,0.3265,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_395,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7896321161852742, 0.05000000074505806, 0.7999870162299847, 0.05000000074505806, 0.7999947117298711, 0.05000000074505806, 0.7999927012085257, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-396,36.27,0.3627,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_396,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.785604292198213, 0.05000000074505806, 0.7999885425478431, 0.05000000074505806, 0.799992903796425, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999948266452237, 0.05000000074505806, 0.800000011920929]"
-397,32.25,0.3225,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_397,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7872163314684072, 0.05000000074505806, 0.7999915026486047, 0.05000000074505806, 0.7999922145159974, 0.05000000074505806, 0.799992398649714, 0.05000000074505806, 0.7999984590164511, 0.05000000074505806, 0.7999953897418766]"
-398,35.260000000000005,0.3526,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_398,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.789039103745824, 0.05000000074505806, 0.7999880597260589, 0.05000000074505806, 0.7999943418309325, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999989751163655]"
-399,35.25,0.3525,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_399,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7851254568956153, 0.05000000074505806, 0.799991152698218, 0.05000000074505806, 0.7999953843685879, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999992190749274]"
-400,23.929999999999996,0.23929999999999996,20301696,50.06534090909091,25.35301237224314,BEST_adc_episode_400,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.71875, 0.21875, 0.75, 0.1875, 0.78125, 0.046875, 0.78125, 0.046875, 0.78125, 0.109375, 0.765625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7310135818253884, 0.05000000074505806, 0.7666119955140845, 0.05000000074505806, 0.7997628135685211, 0.05000000074505806, 0.7959523373277781, 0.05000000074505806, 0.7862820301647143, 0.05000000074505806, 0.7469557136429811]"
-401,26.590000000000003,0.2659,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_401,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7881700740667559, 0.05000000074505806, 0.7999965594384517, 0.05000000074505806, 0.7999948514518118, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999941122028104, 0.05000000074505806, 0.800000011920929]"
-402,34.25,0.3425,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_402,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7891574381750684, 0.05000000074505806, 0.7999974439256874, 0.05000000074505806, 0.7999954653794915, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.799996955828309]"
-403,34.550000000000004,0.34550000000000003,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_403,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7792365509712168, 0.05000000074505806, 0.7999934667636133, 0.05000000074505806, 0.7998877038211495, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999944564165897, 0.05000000074505806, 0.800000011920929]"
-404,35.129999999999995,0.35129999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_404,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7839537739371677, 0.05000000074505806, 0.7999959344437118, 0.05000000074505806, 0.7999969799779136, 0.05000000074505806, 0.7999966796746586, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999955010393404]"
-405,27.280000000000005,0.27280000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_405,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.785137402846372, 0.05000000074505806, 0.7999957028941231, 0.05000000074505806, 0.7999918906633889, 0.05000000074505806, 0.7999951242445023, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999995930755933]"
-406,25.1,0.251,20052288,49.45028409090909,23.45346960731576,BEST_adc_episode_406,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.75, 0.359375, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7310676709758814, 0.05000000074505806, 0.800000011920929, 0.05299507945463607, 0.800000011920929, 0.052865516791276324, 0.800000011920929]"
-407,27.93,0.2793,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_407,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.778083537888325, 0.05000000074505806, 0.7999950253513584, 0.05000000074505806, 0.7998881528209953, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-408,36.67,0.3667,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_408,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7842134268281283, 0.05000000074505806, 0.7999973341114146, 0.05000000074505806, 0.799994021278866, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.799997930814797, 0.05000000074505806, 0.7999983259494959]"
-409,29.090000000000003,0.29090000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_409,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7953775457371487, 0.05000000074505806, 0.7999988887052304, 0.05000000074505806, 0.7999978153475312, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999992016882738, 0.05000000074505806, 0.800000011920929]"
-410,34.17,0.3417,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_410,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7957418227231662, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999965826635471, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999987009318164]"
-411,34.63,0.34630000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_411,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7971156830488503, 0.05000000074505806, 0.7999975210465787, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999983206510569, 0.05000000074505806, 0.7999994245719639]"
-412,35.22,0.3522,20301696,50.06534090909091,25.645508337816032,BEST_adc_episode_412,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.125, 0.78125, 0.046875, 0.796875, 0.046875, 0.765625, 0.046875, 0.765625]","[0.05000000074505806, 0.05424745508304937, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.12541198814555435, 0.7944315026037742, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7752275167028495, 0.05000000074505806, 0.7462451510031299]"
-413,26.759999999999994,0.26759999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_413,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7978118210131537, 0.05000000074505806, 0.7999953328626335, 0.05000000074505806, 0.7999984429447008, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999975053479419, 0.05000000074505806, 0.800000011920929]"
-414,34.84,0.34840000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_414,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.797010997698042, 0.05000000074505806, 0.7999976462034509, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999984773066041, 0.05000000074505806, 0.7999992698722092]"
-415,33.56,0.3356,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_415,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7968350026324562, 0.05000000074505806, 0.7999989893750274, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-416,33.040000000000006,0.3304000000000001,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_416,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7977561098626293, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.799999750784015, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.79999929703308]"
-417,27.569999999999993,0.27569999999999995,17978112,44.33522727272727,24.075443786982248,BEST_adc_episode_417,"[0, 0.0625, 0.0625, 0.0625, 0.125, 0.125, 0.25, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.08301410809589294, 0.06430448312114047, 0.10526229719275751, 0.1708472396998273, 0.14162864970462805, 0.27307329163251637, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999991569080751, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-418,23.87,0.23870000000000002,10387008,25.61505681818182,22.653308230231307,BEST_adc_episode_418,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.125, 0.78125, 0.03125, 0.75, 0.03125, 0.75, 0.046875, 0.796875, 0.0625, 0.765625, 0.0625, 0.78125]","[0.05000000074505806, 0.7942205892355202, 0.05000000074505806, 0.7834269029033132, 0.05000000074505806, 0.790398138031686, 0.05000000074505806, 0.15054522189680253, 0.800000011920929, 0.05000000074505806, 0.7785913327282553, 0.05000000074505806, 0.7656312501479678, 0.05000000074505806, 0.800000011920929, 0.07128239517089559, 0.7706787467792795, 0.06987780852446399, 0.7823845503403885]"
-419,11.670000000000002,0.11670000000000001,9941760,24.517045454545457,21.22444862829478,BEST_adc_episode_419,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.15625, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7643318492911862, 0.05000000074505806, 0.7745425519166926, 0.05000000074505806, 0.7795120072509358, 0.05000000074505806, 0.17373718217125347, 0.7999973315356111, 0.05000000074505806, 0.7999994554677593, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999993662363054]"
-420,19.720000000000006,0.19720000000000007,10743552,26.494318181818183,21.748924152770307,BEST_adc_episode_420,"[0, 0.6875, 0, 0.6875, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7435717500012117, 0.05000000074505806, 0.7379543649154435, 0.05000000074505806, 0.7539013285562609, 0.05000000074505806, 0.05000000074505806, 0.7999857485328777, 0.05000000074505806, 0.7999910450002391, 0.05000000074505806, 0.7999953616802166, 0.05000000074505806, 0.7999985995701382, 0.05000000074505806, 0.7999995326217871, 0.05000000074505806, 0.800000011920929]"
-421,22.340000000000003,0.22340000000000004,10904832,26.892045454545453,26.401963421194193,BEST_adc_episode_421,"[0, 0.75, 0.0625, 0.75, 0, 0.75, 0, 0.53125, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7998725053639164, 0.0652604829242707, 0.7997631341797986, 0.05575776688552881, 0.7998595587953734, 0.05467895703695169, 0.5534282396757505, 0.05000000074505806, 0.7999981974181726, 0.05000000074505806, 0.7999996455601642, 0.05000000074505806, 0.05000000074505806, 0.7999988050372019, 0.05000000074505806, 0.7999986566029397, 0.05000000074505806, 0.799999675903931]"
-422,21.650000000000002,0.21650000000000003,10153728,25.03977272727273,21.53375470683163,BEST_adc_episode_422,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7978410121678494, 0.05000000074505806, 0.7971045866208073, 0.05000000074505806, 0.7960565350004413, 0.05000000074505806, 0.05000000074505806, 0.7999966991145503, 0.05000000074505806, 0.7999947177496847, 0.05000000074505806, 0.7999956893652997, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999995004885495, 0.05000000074505806, 0.800000011920929]"
-423,18.259999999999998,0.18259999999999998,10153728,25.03977272727273,21.53375470683163,BEST_adc_episode_423,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7971152385689193, 0.05000000074505806, 0.7972697652844045, 0.05000000074505806, 0.7967071784905525, 0.05000000074505806, 0.05000000074505806, 0.7999970704029856, 0.05000000074505806, 0.7999998617238065, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-424,15.82,0.1582,10472256,25.82528409090909,22.767616998386227,BEST_adc_episode_424,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.75, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.71875]","[0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7951514591253552, 0.05000000074505806, 0.7958383430024818, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7668145302421555, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7214349895788912]"
-425,20.220000000000006,0.20220000000000005,10153728,25.03977272727273,21.53375470683163,BEST_adc_episode_425,"[0, 0.75, 0, 0.75, 0, 0.75, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7927473017546812, 0.05000000074505806, 0.7925894654437959, 0.05000000074505806, 0.7906149108568892, 0.05000000074505806, 0.05000000074505806, 0.7999978729564534, 0.05000000074505806, 0.799997853924479, 0.05000000074505806, 0.7999987450761028, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999993302280628]"
-426,22.7,0.22699999999999998,10448640,25.767045454545457,21.64133942980097,BEST_adc_episode_426,"[0, 0.75, 0, 0.75, 0, 0.6875, 0, 0.03125, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.7687747782389578, 0.05000000074505806, 0.7590415563459333, 0.05000000074505806, 0.7329612780548628, 0.05000000074505806, 0.05000000074505806, 0.7999960698232065, 0.05000000074505806, 0.7999931325261147, 0.05000000074505806, 0.7999904918557732, 0.05000000074505806, 0.7999985496151761, 0.05000000074505806, 0.7999970006512129, 0.05000000074505806, 0.7999923506271698]"
-427,32.11000000000001,0.32110000000000005,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_427,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.3456561399556784, 0.05000000074505806, 0.6913528022485923, 0.05000000074505806, 0.6441461904966923, 0.05000000074505806, 0.0745923841633943, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-428,34.919999999999995,0.34919999999999995,20130048,49.64204545454545,24.472162452931684,BEST_adc_episode_428,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.71875, 0.25, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.638266202430584, 0.05000000074505806, 0.7343745328960194, 0.05000000074505806, 0.7967848536250949, 0.05000000074505806, 0.7999970198921684, 0.05000000074505806, 0.799995767001398, 0.05000000074505806, 0.7999967021090809]"
-429,34.64,0.3464,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_429,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7682436687787214, 0.05000000074505806, 0.7999970938771918, 0.05000000074505806, 0.7999647931843675, 0.05000000074505806, 0.7999999099940149, 0.05000000074505806, 0.7999993661721732, 0.05000000074505806, 0.7999987580594198]"
-430,33.879999999999995,0.33879999999999993,20286720,50.028409090909086,25.073964497041416,BEST_adc_episode_430,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.71875, 0.21875, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.765625, 0.1875, 0.75]","[0.05845841161132297, 0.05343340814873009, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06170763187615251, 0.05995877717862587, 0.06437156478360563, 0.7311138884009017, 0.055866612642870135, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7777130931788122, 0.05000000074505806, 0.7295103481944176]"
-431,30.459999999999997,0.3046,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_431,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.780581456832934, 0.05000000074505806, 0.7999962426518856, 0.05000000074505806, 0.7999565392100108, 0.05000000074505806, 0.7999991739230847, 0.05000000074505806, 0.7999995754967559, 0.05000000074505806, 0.7999987109144301]"
-432,33.41,0.33409999999999995,20176128,49.75568181818182,24.53940290478752,BEST_adc_episode_432,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.75, 0.03125, 0.78125, 0.15625, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7802051556861941, 0.05000000074505806, 0.7999969179931506, 0.05000000074505806, 0.7999684492180448, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7999995324991562, 0.05000000074505806, 0.800000011920929]"
-433,32.099999999999994,0.32099999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_433,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7874806022825223, 0.05000000074505806, 0.7999980415560857, 0.05000000074505806, 0.7999960169548979, 0.05000000074505806, 0.7999998319667415, 0.05000000074505806, 0.7999993113860548, 0.05000000074505806, 0.799999777792771]"
-434,33.15,0.33149999999999996,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_434,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7886890186991438, 0.05000000074505806, 0.7999972559113242, 0.05000000074505806, 0.7999978102701546, 0.05000000074505806, 0.799999958352687, 0.05000000074505806, 0.799998720558879, 0.05000000074505806, 0.7999990044386908]"
-435,37.529999999999994,0.37529999999999997,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_435,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7903140529760361, 0.05000000074505806, 0.799997468829838, 0.05000000074505806, 0.7999979513219742, 0.05000000074505806, 0.7999993713019258, 0.05000000074505806, 0.7999993112501155, 0.05000000074505806, 0.7999988414459229]"
-436,31.86,0.3186,20197440,49.80823863636364,25.21853146853147,BEST_adc_episode_436,"[0.0625, 0, 0.0625, 0, 0, 0, 0, 0.0625, 0.78125, 0.03125, 0.75, 0.28125, 0.75, 0.046875, 0.796875, 0.046875, 0.765625, 0.1875, 0.796875]","[0.06828761586231404, 0.05000000074505806, 0.06320800099079552, 0.056542158841142394, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.07208712513829861, 0.800000011920929, 0.05000000074505806, 0.7512286993063971, 0.07453747228359388, 0.7766336855468265, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7491686565131257, 0.05000000074505806, 0.800000011920929]"
-437,30.740000000000002,0.3074,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_437,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7904965505800312, 0.05000000074505806, 0.7999977563153233, 0.05000000074505806, 0.7999973122356288, 0.05000000074505806, 0.7999991531023042, 0.05000000074505806, 0.79999913243497, 0.05000000074505806, 0.799998637822624]"
-438,34.48,0.3448,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_438,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7892962361799555, 0.05000000074505806, 0.7999972119269692, 0.05000000074505806, 0.7999969351183829, 0.05000000074505806, 0.7999979525858352, 0.05000000074505806, 0.7999962349310435, 0.05000000074505806, 0.7999971113326447]"
-439,31.320000000000004,0.31320000000000003,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_439,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7885912334179074, 0.05000000074505806, 0.7999975176152849, 0.05000000074505806, 0.7999967064547444, 0.05000000074505806, 0.799999200263333, 0.05000000074505806, 0.799998647145939, 0.05000000074505806, 0.7999988606834695]"
-440,31.45,0.3145,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_440,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7933975973468308, 0.05000000074505806, 0.7999981036950007, 0.05000000074505806, 0.7999978447936549, 0.05000000074505806, 0.799999124588501, 0.05000000074505806, 0.7999982294472272, 0.05000000074505806, 0.7999982864151014]"
-441,35.62,0.35619999999999996,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_441,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7909472202302286, 0.05000000074505806, 0.7999969798183215, 0.05000000074505806, 0.7999973401475179, 0.05000000074505806, 0.7999975486154352, 0.05000000074505806, 0.7999967651136248, 0.05000000074505806, 0.7999970792022826]"
-442,29.500000000000004,0.29500000000000004,20170368,49.74147727272727,24.53604088219473,BEST_adc_episode_442,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.78125, 0.03125, 0.78125, 0.09375, 0.78125, 0.09375, 0.765625, 0.1875, 0.796875, 0.046875, 0.796875]","[0.06399303725696005, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7409444590683348, 0.05000000074505806, 0.7780299944203913, 0.05000000074505806, 0.7463799150270998, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.800000011920929]"
-443,30.589999999999996,0.30589999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_443,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7915509713030638, 0.05000000074505806, 0.7999974765232473, 0.05000000074505806, 0.7999978248792937, 0.05000000074505806, 0.7999976848335345, 0.05000000074505806, 0.7999972992960511, 0.05000000074505806, 0.799997147513734]"
-444,21.699999999999996,0.21699999999999997,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_444,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7915255761485928, 0.05000000074505806, 0.7999971594597894, 0.05000000074505806, 0.7999974277750687, 0.05000000074505806, 0.7999970940720463, 0.05000000074505806, 0.7999970175165753, 0.05000000074505806, 0.7999974297669901]"
-445,29.190000000000005,0.29190000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_445,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7939858788366945, 0.05000000074505806, 0.7999978544074159, 0.05000000074505806, 0.7999981345351398, 0.05000000074505806, 0.7999986168417826, 0.05000000074505806, 0.7999983218733925, 0.05000000074505806, 0.7999988426465264]"
-446,32.31,0.3231,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_446,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7958573131881596, 0.05000000074505806, 0.799998236855509, 0.05000000074505806, 0.799998834127174, 0.05000000074505806, 0.7999986269330843, 0.05000000074505806, 0.799998771305092, 0.05000000074505806, 0.7999988380142625]"
-447,33.730000000000004,0.33730000000000004,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_447,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.79716086653438, 0.05000000074505806, 0.7999988756157029, 0.05000000074505806, 0.7999989434473345, 0.05000000074505806, 0.799998708659243, 0.05000000074505806, 0.7999983418140966, 0.05000000074505806, 0.7999985405329318]"
-448,38.92,0.3892,20225664,49.87784090909091,25.100860677783754,BEST_adc_episode_448,"[0, 0, 0, 0.0625, 0, 0, 0.0625, 0.03125, 0.78125, 0.03125, 0.78125, 0.0625, 0.78125, 0.140625, 0.796875, 0.046875, 0.796875, 0.046875, 0.765625]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.0806765587002175, 0.0518962400368373, 0.05000000074505806, 0.0667363780009411, 0.05000000074505806, 0.800000011920929, 0.05628101722158528, 0.800000011920929, 0.05000000074505806, 0.7846215683899614, 0.05000000074505806, 0.800000011920929, 0.057217929772254705, 0.800000011920929, 0.05000000074505806, 0.7730718484651471]"
-449,33.919999999999995,0.33919999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_449,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7977004484078197, 0.05000000074505806, 0.7999990592686992, 0.05000000074505806, 0.7999991861681655, 0.05000000074505806, 0.7999988416937497, 0.05000000074505806, 0.7999980991403961, 0.05000000074505806, 0.7999978607719861]"
-450,35.69,0.3569,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_450,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7960123152401579, 0.05000000074505806, 0.7999981852724485, 0.05000000074505806, 0.799998158843478, 0.05000000074505806, 0.7999948497848868, 0.05000000074505806, 0.799993462722177, 0.05000000074505806, 0.7999945515801915]"
-451,35.23,0.35229999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_451,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7966803801541955, 0.05000000074505806, 0.7999984363653277, 0.05000000074505806, 0.799998559407098, 0.05000000074505806, 0.7999970150073876, 0.05000000074505806, 0.7999966203684199, 0.05000000074505806, 0.7999967591745814]"
-452,33.52,0.33520000000000005,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_452,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7973964559926877, 0.05000000074505806, 0.7999987268949159, 0.05000000074505806, 0.7999984585045538, 0.05000000074505806, 0.7999973005218068, 0.05000000074505806, 0.7999965539521066, 0.05000000074505806, 0.7999966823096363]"
-453,32.54,0.32539999999999997,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_453,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7970914884715619, 0.05000000074505806, 0.7999985939469403, 0.05000000074505806, 0.7999983889264558, 0.05000000074505806, 0.7999954378046267, 0.05000000074505806, 0.7999938673104952, 0.05000000074505806, 0.7999943056668103]"
-454,34.41,0.34409999999999996,20276928,50.00426136363636,25.61188811188811,BEST_adc_episode_454,"[0.0625, 0, 0, 0, 0, 0, 0, 0.21875, 0.78125, 0.0625, 0.75, 0.28125, 0.78125, 0.046875, 0.734375, 0.296875, 0.703125, 0.359375, 0.6875]","[0.07446921662683668, 0.05000000074505806, 0.05000000074505806, 0.05098161244351964, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.800000011920929, 0.08278817335768397, 0.7486457476200891, 0.05000000074505806, 0.800000011920929, 0.05000000074505806, 0.7488930345684415, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-455,34.31999999999999,0.34319999999999995,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_455,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7961644733681807, 0.05000000074505806, 0.799998038144816, 0.05000000074505806, 0.7999978099060868, 0.05000000074505806, 0.7999933615361641, 0.05000000074505806, 0.7999913520487278, 0.05000000074505806, 0.7999921802136476]"
-456,35.199999999999996,0.352,20187648,49.784090909090914,24.556213017751478,BEST_adc_episode_456,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.78125, 0.03125, 0.78125, 0.03125, 0.78125, 0.046875, 0.796875, 0.046875, 0.796875, 0.046875, 0.796875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7953828476139988, 0.05000000074505806, 0.7999976186108645, 0.05000000074505806, 0.7999974621953512, 0.05000000074505806, 0.7999910308040953, 0.05000000074505806, 0.7999859852253204, 0.05000000074505806, 0.7999827018253429]"
-457,45.07,0.4507,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_457,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.7154853222993692, 0.05000000074505806, 0.6895947395192101, 0.05000000074505806, 0.14022163752213934, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-458,30.620000000000005,0.3062,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_458,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-459,34.309999999999995,0.34309999999999996,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_459,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806]"
-460,30.56,0.3056,20286720,50.028409090909086,25.013448090371167,BEST_adc_episode_460,"[0, 0, 0, 0, 0, 0, 0, 0.375, 0.6875, 0.34375, 0.6875, 0.375, 0.6875, 0.375, 0.671875, 0.390625, 0.671875, 0.390625, 0.671875]","[0.05384214474699749, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06765110679601997, 0.08839463625152358, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.05000000074505806, 0.06246037657065524]"
diff --git a/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/coach/worker_0.simple_rl_graph.main_level.main_level.agent_0.csv b/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/coach/worker_0.simple_rl_graph.main_level.main_level.agent_0.csv
deleted file mode 100644
index 934a661..0000000
--- a/examples/automated_deep_compression/sample_logs/coach_rl/AMC-Plain20___2018.12.29-033839/coach/worker_0.simple_rl_graph.main_level.main_level.agent_0.csv
+++ /dev/null
@@ -1,401 +0,0 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Wall-Clock Time,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,TD targets/Mean,TD targets/Stdev,TD targets/Max,TD targets/Min,actions/Mean,actions/Stdev,actions/Max,actions/Min
-1,0.0,1.0,19.0,1.0,19.0,19.0,0.5,,,0.0,0.0,,,,,,,,,,,,,,,,0.2014,0.0,0.2014,0.2014,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,0.0,1.0,38.0,2.0,19.0,38.0,0.5,,,0.0,10.261255979537964,,,,,,,,,,,,,,,,0.28819999999999996,5.551115123125783e-17,0.2882,0.2882,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,0.0,1.0,57.0,3.0,19.0,57.0,0.5,,,0.0,20.061906099319458,,,,,,,,,,,,,,,,0.2935999999999999,5.551115123125783e-17,0.29359999999999997,0.29359999999999997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,0.0,1.0,76.0,4.0,19.0,76.0,0.5,,,0.0,30.91125178337097,,,,,,,,,,,,,,,,0.5588,0.0,0.5588,0.5588,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,0.0,1.0,95.0,5.0,19.0,95.0,0.5,,,0.0,41.31717276573181,,,,,,,,,,,,,,,,0.258,0.0,0.258,0.258,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-6,0.0,1.0,114.0,6.0,19.0,114.0,0.5,,,0.0,51.05343198776245,,,,,,,,,,,,,,,,0.21030000000000007,2.7755575615628914e-17,0.21030000000000004,0.21030000000000004,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,0.0,1.0,133.0,7.0,19.0,133.0,0.5,,,0.0,60.40221428871155,,,,,,,,,,,,,,,,0.2237,0.0,0.2237,0.2237,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,0.0,1.0,152.0,8.0,19.0,152.0,0.5,,,0.0,70.65434217453003,,,,,,,,,,,,,,,,0.3834999999999999,5.551115123125783e-17,0.38349999999999995,0.38349999999999995,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,0.0,1.0,171.0,9.0,19.0,171.0,0.5,,,0.0,79.8943510055542,,,,,,,,,,,,,,,,0.22550000000000006,2.7755575615628914e-17,0.22550000000000003,0.22550000000000003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,0.0,1.0,190.0,10.0,19.0,190.0,0.5,,,0.0,89.20587801933289,,,,,,,,,,,,,,,,0.1271,0.0,0.1271,0.1271,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,0.0,1.0,209.0,11.0,19.0,209.0,0.5,,,0.0,98.89018726348877,,,,,,,,,,,,,,,,0.16449999999999995,2.7755575615628914e-17,0.16449999999999998,0.16449999999999998,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-12,0.0,1.0,228.0,12.0,19.0,228.0,0.5,,,0.0,990.6465878486633,,,,,,,,,,,,,,,,0.3866,5.551115123125783e-17,0.38660000000000005,0.38660000000000005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,0.0,1.0,247.0,13.0,19.0,247.0,0.5,,,0.0,1000.4312076568604,,,,,,,,,,,,,,,,0.4033999999999999,5.551115123125783e-17,0.4034,0.4034,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,0.0,1.0,266.0,14.0,19.0,266.0,0.5,,,0.0,1009.7538480758667,,,,,,,,,,,,,,,,0.41120000000000007,0.0,0.41120000000000007,0.41120000000000007,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,0.0,1.0,285.0,15.0,19.0,285.0,0.5,,,0.0,1020.0048975944519,,,,,,,,,,,,,,,,0.2431,2.7755575615628914e-17,0.24309999999999998,0.24309999999999998,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-17,0.0,1.0,323.0,17.0,19.0,323.0,0.5,,,0.0,1038.9585967063904,,,,,,,,,,,,,,,,0.29990000000000006,0.0,0.29990000000000006,0.29990000000000006,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-22,0.0,1.0,418.0,22.0,19.0,418.0,0.5,,,0.0,1089.285561800003,,,,,,,,,,,,,,,,0.3231,0.0,0.3231,0.3231,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-23,0.0,1.0,437.0,23.0,19.0,437.0,0.5,,,0.0,1099.5737388134003,,,,,,,,,,,,,,,,0.3554000000000001,5.551115123125783e-17,0.35540000000000005,0.35540000000000005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-26,0.0,1.0,494.0,26.0,19.0,494.0,0.5,,,0.0,1130.8715002536774,,,,,,,,,,,,,,,,0.3094,0.0,0.3094,0.3094,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-27,0.0,1.0,513.0,27.0,19.0,513.0,0.5,,,0.0,1140.5225212574005,,,,,,,,,,,,,,,,0.27240000000000003,5.551115123125783e-17,0.2724,0.2724,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-28,0.0,1.0,532.0,28.0,19.0,532.0,0.5,,,0.0,1150.59606051445,,,,,,,,,,,,,,,,0.1369,2.7755575615628914e-17,0.13690000000000002,0.13690000000000002,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-29,0.0,1.0,551.0,29.0,19.0,551.0,0.5,,,0.0,1161.2081112861633,,,,,,,,,,,,,,,,0.3814,5.551115123125783e-17,0.38139999999999996,0.38139999999999996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-30,0.0,1.0,570.0,30.0,19.0,570.0,0.5,,,0.0,1172.6194095611572,,,,,,,,,,,,,,,,0.18420000000000006,2.7755575615628914e-17,0.18420000000000003,0.18420000000000003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-31,0.0,1.0,589.0,31.0,19.0,589.0,0.5,,,0.0,1183.7633545398712,,,,,,,,,,,,,,,,0.4266,0.0,0.4266,0.4266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-32,0.0,1.0,608.0,32.0,19.0,608.0,0.5,,,0.0,1193.4352798461914,,,,,,,,,,,,,,,,0.4050000000000001,5.551115123125783e-17,0.405,0.405,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-33,0.0,1.0,627.0,33.0,19.0,627.0,0.5,,,0.0,1202.9237990379333,,,,,,,,,,,,,,,,0.21950000000000003,0.0,0.21950000000000003,0.21950000000000003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-34,0.0,1.0,646.0,34.0,19.0,646.0,0.5,,,0.0,1212.6369416713715,,,,,,,,,,,,,,,,0.4320000000000001,5.551115123125783e-17,0.43200000000000005,0.43200000000000005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-35,0.0,1.0,665.0,35.0,19.0,665.0,0.5,,,0.0,1222.3818759918213,,,,,,,,,,,,,,,,0.4477,0.0,0.4477,0.4477,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-36,0.0,1.0,684.0,36.0,19.0,684.0,0.5,,,0.0,1231.7433679103851,,,,,,,,,,,,,,,,0.3746000000000001,0.0,0.3746000000000001,0.3746000000000001,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-37,0.0,1.0,703.0,37.0,19.0,703.0,0.5,,,0.0,1241.5960748195648,,,,,,,,,,,,,,,,0.15430000000000002,2.7755575615628914e-17,0.1543,0.1543,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-38,0.0,1.0,722.0,38.0,19.0,722.0,0.5,,,0.0,1250.878069639206,,,,,,,,,,,,,,,,0.5294000000000001,1.1102230246251565e-16,0.5294,0.5294,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-39,0.0,1.0,741.0,39.0,19.0,741.0,0.5,,,0.0,1261.2056059837341,,,,,,,,,,,,,,,,0.12250000000000005,0.0,0.12250000000000005,0.12250000000000005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-40,0.0,1.0,760.0,40.0,19.0,760.0,0.5,,,0.0,1271.03728890419,,,,,,,,,,,,,,,,0.24039999999999997,2.7755575615628914e-17,0.24039999999999995,0.24039999999999995,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-41,0.0,1.0,779.0,41.0,19.0,779.0,0.5,,,0.0,1280.9403886795044,,,,,,,,,,,,,,,,0.2794999999999999,5.551115123125783e-17,0.27949999999999997,0.27949999999999997,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-42,0.0,1.0,798.0,42.0,19.0,798.0,0.5,,,0.0,1291.6593778133392,,,,,,,,,,,,,,,,0.34409999999999996,0.0,0.34409999999999996,0.34409999999999996,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-43,0.0,1.0,817.0,43.0,19.0,817.0,0.5,,,0.0,1301.5895113945007,,,,,,,,,,,,,,,,0.1953,0.0,0.1953,0.1953,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-44,0.0,1.0,836.0,44.0,19.0,836.0,0.5,,,0.0,1311.186546087265,,,,,,,,,,,,,,,,0.11939999999999991,2.7755575615628914e-17,0.11939999999999994,0.11939999999999994,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-45,0.0,1.0,855.0,45.0,19.0,855.0,0.5,,,0.0,1320.5518448352814,,,,,,,,,,,,,,,,0.49700000000000005,0.0,0.49700000000000005,0.49700000000000005,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-46,0.0,1.0,874.0,46.0,19.0,874.0,0.5,,,0.0,1330.0690953731537,,,,,,,,,,,,,,,,0.2714,0.0,0.2714,0.2714,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-47,0.0,1.0,893.0,47.0,19.0,893.0,0.5,,,0.0,1339.7240817546844,,,,,,,,,,,,,,,,0.2972,0.0,0.2972,0.2972,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-48,0.0,1.0,912.0,48.0,19.0,912.0,0.5,,,0.0,1349.932431936264,,,,,,,,,,,,,,,,0.112,1.3877787807814457e-17,0.11199999999999999,0.11199999999999999,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-49,0.0,1.0,931.0,49.0,19.0,931.0,0.5,,,0.0,1360.1766250133514,,,,,,,,,,,,,,,,0.39920000000000005,5.551115123125783e-17,0.3992,0.3992,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-50,0.0,1.0,950.0,50.0,19.0,950.0,0.5,,,0.0,1370.900658607483,,,,,,,,,,,,,,,,0.2611,0.0,0.2611,0.2611,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-51,0.0,1.0,969.0,51.0,19.0,969.0,0.5,,,0.0,1381.8852503299713,,,,,,,,,,,,,,,,0.40090000000000003,0.0,0.40090000000000003,0.40090000000000003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/examples/classifier_compression/compress_classifier.py b/examples/classifier_compression/compress_classifier.py
index 2a8d5bc..c25c03f 100755
--- a/examples/classifier_compression/compress_classifier.py
+++ b/examples/classifier_compression/compress_classifier.py
@@ -51,197 +51,72 @@ models, or with the provided sample models:
- MobileNet for ImageNet: https://github.com/marvis/pytorch-mobilenet
"""
-import math
-import time
-import os
import traceback
import logging
-from collections import OrderedDict
from functools import partial
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.parallel
-import torch.backends.cudnn as cudnn
-import torch.optim
-import torch.utils.data
-import torchnet.meter as tnt
import distiller
+from distiller.models import create_model
+import distiller.apputils.image_classifier as classifier
import distiller.apputils as apputils
-import distiller.model_summaries as model_summaries
-from distiller.data_loggers import *
-import distiller.quantization as quantization
-import examples.automated_deep_compression as adc
-from distiller.models import ALL_MODEL_NAMES, create_model
import parser
-import operator
+import os
+import numpy as np
# Logger handle
-msglogger = None
+msglogger = logging.getLogger()
def main():
- script_dir = os.path.dirname(__file__)
- module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
- global msglogger
-
# Parse arguments
- args = parser.get_parser().parse_args()
- if args.epochs is None:
- args.epochs = 90
-
- if not os.path.exists(args.output_dir):
- os.makedirs(args.output_dir)
- msglogger = apputils.config_pylogger(os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir,
- args.verbose)
-
- # Log various details about the execution environment. It is sometimes useful
- # to refer to past experiment executions and this information may be useful.
- apputils.log_execution_env_state(
- filter(None, [args.compress, args.qe_stats_file]), # remove both None and empty strings
- msglogger.logdir, gitroot=module_path)
- msglogger.debug("Distiller: %s", distiller.__version__)
-
- if args.evaluate:
- args.deterministic = True
- if args.deterministic:
- distiller.set_deterministic(args.seed) # For experiment reproducability
- else:
- if args.seed is not None:
- distiller.set_seed(args.seed)
- # Turn on CUDNN benchmark mode for best performance. This is usually "safe" for image
- # classification models, as the input sizes don't change during the run
- # See here: https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3
- cudnn.benchmark = True
-
- start_epoch = 0
- ending_epoch = args.epochs
- perf_scores_history = []
-
- if args.cpu or not torch.cuda.is_available():
- # Set GPU index to -1 if using CPU
- args.device = 'cpu'
- args.gpus = -1
- else:
- args.device = 'cuda'
- if args.gpus is not None:
- try:
- args.gpus = [int(s) for s in args.gpus.split(',')]
- except ValueError:
- raise ValueError('ERROR: Argument --gpus must be a comma-separated list of integers only')
- available_gpus = torch.cuda.device_count()
- for dev_id in args.gpus:
- if dev_id >= available_gpus:
- raise ValueError('ERROR: GPU device ID {0} requested, but only {1} devices available'
- .format(dev_id, available_gpus))
- # Set default device in case the first one on the list != 0
- torch.cuda.set_device(args.gpus[0])
-
- # Infer the dataset from the model name
- args.dataset = distiller.apputils.classification_dataset_str_from_arch(args.arch)
- args.num_classes = distiller.apputils.classification_num_classes(args.dataset)
-
- if args.earlyexit_thresholds:
- args.num_exits = len(args.earlyexit_thresholds) + 1
- args.loss_exits = [0] * args.num_exits
- args.losses_exits = []
- args.exiterrors = []
-
- # Create the model
- model = create_model(args.pretrained, args.dataset, args.arch,
- parallel=not args.load_serialized, device_ids=args.gpus)
- compression_scheduler = None
- # Create a couple of logging backends. TensorBoardLogger writes log files in a format
- # that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
- tflogger = TensorBoardLogger(msglogger.logdir)
- pylogger = PythonLogger(msglogger)
-
- # capture thresholds for early-exit training
- if args.earlyexit_thresholds:
- msglogger.info('=> using early-exit threshold values of %s', args.earlyexit_thresholds)
-
- # TODO(barrh): args.deprecated_resume is deprecated since v0.3.1
- if args.deprecated_resume:
- msglogger.warning('The "--resume" flag is deprecated. Please use "--resume-from=YOUR_PATH" instead.')
- if not args.reset_optimizer:
- msglogger.warning('If you wish to also reset the optimizer, call with: --reset-optimizer')
- args.reset_optimizer = True
- args.resumed_checkpoint_path = args.deprecated_resume
-
- # We can optionally resume from a checkpoint
- optimizer = None
- if args.resumed_checkpoint_path:
- model, compression_scheduler, optimizer, start_epoch = apputils.load_checkpoint(
- model, args.resumed_checkpoint_path, model_device=args.device)
- elif args.load_model_path:
- model = apputils.load_lean_checkpoint(model, args.load_model_path,
- model_device=args.device)
- if args.reset_optimizer:
- start_epoch = 0
- if optimizer is not None:
- optimizer = None
- msglogger.info('\nreset_optimizer flag set: Overriding resumed optimizer and resetting epoch count to 0')
-
- # Define loss function (criterion)
- criterion = nn.CrossEntropyLoss().to(args.device)
+ args = parser.add_cmdline_args(classifier.init_classifier_compression_arg_parser()).parse_args()
+ app = ClassifierCompressorSampleApp(args, script_dir=os.path.dirname(__file__))
+ if app.handle_subapps():
+ return
+ init_knowledge_distillation(app.args, app.model, app.compression_scheduler)
+ app.run_training_loop()
+ # Finally run results on the test set
+ return app.test()
- if optimizer is None:
- optimizer = torch.optim.SGD(model.parameters(),
- lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
- msglogger.info('Optimizer Type: %s', type(optimizer))
- msglogger.info('Optimizer Args: %s', optimizer.defaults)
+
+def handle_subapps(model, criterion, optimizer, compression_scheduler, pylogger, args):
+ def load_test_data(args):
+ test_loader = classifier.load_data(args, load_train=False, load_val=False, load_test=True)
+ return test_loader
- if args.AMC:
- return automated_deep_compression(model, criterion, optimizer, pylogger, args)
+ do_exit = False
if args.greedy:
- return greedy(model, criterion, optimizer, pylogger, args)
-
- # This sample application can be invoked to produce various summary reports.
- if args.summary:
+ greedy(model, criterion, optimizer, pylogger, args)
+ do_exit = True
+ elif args.summary:
+ # This sample application can be invoked to produce various summary reports
for summary in args.summary:
distiller.model_summary(model, summary, args.dataset)
- return
-
- if args.export_onnx is not None:
- return distiller.export_img_classifier_to_onnx(model,
- os.path.join(msglogger.logdir, args.export_onnx),
- args.dataset, add_softmax=True, verbose=False)
-
- if args.qe_calibration:
- return acts_quant_stats_collection(model, criterion, pylogger, args)
-
- if args.activation_histograms:
- return acts_histogram_collection(model, criterion, pylogger, args)
-
- activations_collectors = create_activation_stats_collectors(model, *args.activation_stats)
-
- # Load the datasets: the dataset to load is inferred from the model name passed
- # in args.arch. The default dataset is ImageNet, but if args.arch contains the
- # substring "_cifar", then cifar10 is used.
- train_loader, val_loader, test_loader, _ = load_data(args)
- msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
- len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler))
-
- if args.sensitivity is not None:
- sensitivities = np.arange(args.sensitivity_range[0], args.sensitivity_range[1], args.sensitivity_range[2])
- return sensitivity_analysis(model, criterion, test_loader, pylogger, args, sensitivities)
-
- if args.evaluate:
- return evaluate_model(model, criterion, test_loader, pylogger, activations_collectors, args,
- compression_scheduler)
-
- if args.compress:
- # The main use-case for this sample application is CNN compression. Compression
- # requires a compression schedule configuration file in YAML.
- compression_scheduler = distiller.file_config(model, optimizer, args.compress, compression_scheduler,
- (start_epoch-1) if args.resumed_checkpoint_path else None)
- # Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
- model.to(args.device)
- elif compression_scheduler is None:
- compression_scheduler = distiller.CompressionScheduler(model)
-
- if args.thinnify:
+ do_exit = True
+ elif args.export_onnx is not None:
+ distiller.export_img_classifier_to_onnx(model,
+ os.path.join(msglogger.logdir, args.export_onnx),
+ args.dataset, add_softmax=True, verbose=False)
+ do_exit = True
+ elif args.qe_calibration:
+ classifier.acts_quant_stats_collection(model, criterion, pylogger, args)
+ do_exit = True
+ elif args.activation_histograms:
+ classifier.acts_histogram_collection(model, criterion, pylogger, args)
+ do_exit = True
+ elif args.sensitivity is not None:
+ test_loader = load_test_data(args)
+ #sensitivities = np.arange(args.sensitivity_range[0], args.sensitivity_range[1], args.sensitivity_range[2])
+ sensitivities = np.arange(*args.sensitivity_range)
+ sensitivity_analysis(model, criterion, test_loader, pylogger, args, sensitivities)
+ do_exit = True
+ elif args.evaluate:
+ test_loader = load_test_data(args)
+ activations_collectors = classifier.create_activation_stats_collectors(model, *args.activation_stats)
+ classifier.evaluate_model(model, criterion, test_loader, pylogger, activations_collectors,
+ args, compression_scheduler)
+ do_exit = True
+ elif args.thinnify:
#zeros_mask_dict = distiller.create_model_masks_dict(model)
assert args.resumed_checkpoint_path is not None, \
"You must use --resume-from to provide a checkpoint file to thinnify"
@@ -249,9 +124,12 @@ def main():
apputils.save_checkpoint(0, args.arch, model, optimizer=None, scheduler=compression_scheduler,
name="{}_thinned".format(args.resumed_checkpoint_path.replace(".pth.tar", "")),
dir=msglogger.logdir)
- print("Note: your model may have collapsed to random inference, so you may want to fine-tune")
- return
+ msglogger.info("Note: if your model collapsed to random inference, you may want to fine-tune")
+ do_exit = True
+ return do_exit
+
+def init_knowledge_distillation(args, model, compression_scheduler):
args.kd_policy = None
if args.kd_teacher:
teacher = create_model(args.kd_pretrained, args.dataset, args.kd_teacher, device_ids=args.gpus)
@@ -261,7 +139,6 @@ def main():
args.kd_policy = distiller.KnowledgeDistillationPolicy(model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(args.kd_policy, starting_epoch=args.kd_start_epoch, ending_epoch=args.epochs,
frequency=1)
-
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
@@ -269,386 +146,25 @@ def main():
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
- if start_epoch >= ending_epoch:
- msglogger.error(
- 'epoch count is too low, starting epoch is {} but total epochs set to {}'.format(
- start_epoch, ending_epoch))
- raise ValueError('Epochs parameter is too low. Nothing to do.')
- for epoch in range(start_epoch, ending_epoch):
- # This is the main training loop.
- msglogger.info('\n')
- if compression_scheduler:
- compression_scheduler.on_epoch_begin(epoch)
-
- # Train for one epoch
- with collectors_context(activations_collectors["train"]) as collectors:
- train(train_loader, model, criterion, optimizer, epoch, compression_scheduler,
- loggers=[tflogger, pylogger], args=args)
- distiller.log_weights_sparsity(model, epoch, loggers=[tflogger, pylogger])
- distiller.log_activation_statsitics(epoch, "train", loggers=[tflogger],
- collector=collectors["sparsity"])
- if args.masks_sparsity:
- msglogger.info(distiller.masks_sparsity_tbl_summary(model, compression_scheduler))
-
- # evaluate on validation set
- with collectors_context(activations_collectors["valid"]) as collectors:
- top1, top5, vloss = validate(val_loader, model, criterion, [pylogger], args, epoch)
- distiller.log_activation_statsitics(epoch, "valid", loggers=[tflogger],
- collector=collectors["sparsity"])
- save_collectors_data(collectors, msglogger.logdir)
-
- stats = ('Performance/Validation/',
- OrderedDict([('Loss', vloss),
- ('Top1', top1),
- ('Top5', top5)]))
- distiller.log_training_progress(stats, None, epoch, steps_completed=0, total_steps=1, log_freq=1,
- loggers=[tflogger])
-
- if compression_scheduler:
- compression_scheduler.on_epoch_end(epoch, optimizer, metrics={'min': vloss, 'max': top1})
-
- # Update the list of top scores achieved so far, and save the checkpoint
- update_training_scores_history(perf_scores_history, model, top1, top5, epoch, args.num_best_scores)
- is_best = epoch == perf_scores_history[0].epoch
- checkpoint_extras = {'current_top1': top1,
- 'best_top1': perf_scores_history[0].top1,
- 'best_epoch': perf_scores_history[0].epoch}
- apputils.save_checkpoint(epoch, args.arch, model, optimizer=optimizer, scheduler=compression_scheduler,
- extras=checkpoint_extras, is_best=is_best, name=args.name, dir=msglogger.logdir)
-
- # Finally run results on the test set
- test(test_loader, model, criterion, [pylogger], activations_collectors, args=args)
-
-
-OVERALL_LOSS_KEY = 'Overall Loss'
-OBJECTIVE_LOSS_KEY = 'Objective Loss'
-
-
-def train(train_loader, model, criterion, optimizer, epoch,
- compression_scheduler, loggers, args):
- """Training loop for one epoch."""
- losses = OrderedDict([(OVERALL_LOSS_KEY, tnt.AverageValueMeter()),
- (OBJECTIVE_LOSS_KEY, tnt.AverageValueMeter())])
-
- classerr = tnt.ClassErrorMeter(accuracy=True, topk=(1, 5))
- batch_time = tnt.AverageValueMeter()
- data_time = tnt.AverageValueMeter()
-
- # For Early Exit, we define statistics for each exit
- # So exiterrors is analogous to classerr for the non-Early Exit case
- if args.earlyexit_lossweights:
- args.exiterrors = []
- for exitnum in range(args.num_exits):
- args.exiterrors.append(tnt.ClassErrorMeter(accuracy=True, topk=(1, 5)))
-
- total_samples = len(train_loader.sampler)
- batch_size = train_loader.batch_size
- steps_per_epoch = math.ceil(total_samples / batch_size)
- msglogger.info('Training epoch: %d samples (%d per mini-batch)', total_samples, batch_size)
-
- # Switch to train mode
- model.train()
- acc_stats = []
- end = time.time()
- for train_step, (inputs, target) in enumerate(train_loader):
- # Measure data loading time
- data_time.add(time.time() - end)
- inputs, target = inputs.to(args.device), target.to(args.device)
-
- # Execute the forward phase, compute the output and measure loss
- if compression_scheduler:
- compression_scheduler.on_minibatch_begin(epoch, train_step, steps_per_epoch, optimizer)
-
- if not hasattr(args, 'kd_policy') or args.kd_policy is None:
- output = model(inputs)
- else:
- output = args.kd_policy.forward(inputs)
-
- if not args.earlyexit_lossweights:
- loss = criterion(output, target)
- # Measure accuracy
- classerr.add(output.data, target)
- acc_stats.append([classerr.value(1), classerr.value(5)])
- else:
- # Measure accuracy and record loss
- loss = earlyexit_loss(output, target, criterion, args)
- # Record loss
- losses[OBJECTIVE_LOSS_KEY].add(loss.item())
-
- if compression_scheduler:
- # Before running the backward phase, we allow the scheduler to modify the loss
- # (e.g. add regularization loss)
- agg_loss = compression_scheduler.before_backward_pass(epoch, train_step, steps_per_epoch, loss,
- optimizer=optimizer, return_loss_components=True)
- loss = agg_loss.overall_loss
- losses[OVERALL_LOSS_KEY].add(loss.item())
-
- for lc in agg_loss.loss_components:
- if lc.name not in losses:
- losses[lc.name] = tnt.AverageValueMeter()
- losses[lc.name].add(lc.value.item())
- else:
- losses[OVERALL_LOSS_KEY].add(loss.item())
-
- # Compute the gradient and do SGD step
- optimizer.zero_grad()
- loss.backward()
- if compression_scheduler:
- compression_scheduler.before_parameter_optimization(epoch, train_step, steps_per_epoch, optimizer)
- optimizer.step()
- if compression_scheduler:
- compression_scheduler.on_minibatch_end(epoch, train_step, steps_per_epoch, optimizer)
-
- # measure elapsed time
- batch_time.add(time.time() - end)
- steps_completed = (train_step+1)
-
- if steps_completed % args.print_freq == 0:
- # Log some statistics
- errs = OrderedDict()
- if not args.earlyexit_lossweights:
- errs['Top1'] = classerr.value(1)
- errs['Top5'] = classerr.value(5)
- else:
- # for Early Exit case, the Top1 and Top5 stats are computed for each exit.
- for exitnum in range(args.num_exits):
- errs['Top1_exit' + str(exitnum)] = args.exiterrors[exitnum].value(1)
- errs['Top5_exit' + str(exitnum)] = args.exiterrors[exitnum].value(5)
- stats_dict = OrderedDict()
- for loss_name, meter in losses.items():
- stats_dict[loss_name] = meter.mean
- stats_dict.update(errs)
- stats_dict['LR'] = optimizer.param_groups[0]['lr']
- stats_dict['Time'] = batch_time.mean
- stats = ('Performance/Training/', stats_dict)
-
- params = model.named_parameters() if args.log_params_histograms else None
- distiller.log_training_progress(stats,
- params,
- epoch, steps_completed,
- steps_per_epoch, args.print_freq,
- loggers)
- end = time.time()
- return acc_stats
-
-
-def validate(val_loader, model, criterion, loggers, args, epoch=-1):
- """Model validation"""
- if epoch > -1:
- msglogger.info('--- validate (epoch=%d)-----------', epoch)
- else:
- msglogger.info('--- validate ---------------------')
- return _validate(val_loader, model, criterion, loggers, args, epoch)
-
-
-def test(test_loader, model, criterion, loggers, activations_collectors, args):
- """Model Test"""
- msglogger.info('--- test ---------------------')
- if activations_collectors is None:
- activations_collectors = create_activation_stats_collectors(model, None)
- with collectors_context(activations_collectors["test"]) as collectors:
- top1, top5, lossses = _validate(test_loader, model, criterion, loggers, args)
- distiller.log_activation_statsitics(-1, "test", loggers, collector=collectors['sparsity'])
- save_collectors_data(collectors, msglogger.logdir)
- return top1, top5, lossses
-
-
-def _validate(data_loader, model, criterion, loggers, args, epoch=-1):
- """Execute the validation/test loop."""
- losses = {'objective_loss': tnt.AverageValueMeter()}
- classerr = tnt.ClassErrorMeter(accuracy=True, topk=(1, 5))
-
- if args.earlyexit_thresholds:
- # for Early Exit, we have a list of errors and losses for each of the exits.
- args.exiterrors = []
- args.losses_exits = []
- for exitnum in range(args.num_exits):
- args.exiterrors.append(tnt.ClassErrorMeter(accuracy=True, topk=(1, 5)))
- args.losses_exits.append(tnt.AverageValueMeter())
- args.exit_taken = [0] * args.num_exits
-
- batch_time = tnt.AverageValueMeter()
- total_samples = len(data_loader.sampler)
- batch_size = data_loader.batch_size
- if args.display_confusion:
- confusion = tnt.ConfusionMeter(args.num_classes)
- total_steps = total_samples / batch_size
- msglogger.info('%d samples (%d per mini-batch)', total_samples, batch_size)
-
- # Switch to evaluation mode
- model.eval()
-
- end = time.time()
- for validation_step, (inputs, target) in enumerate(data_loader):
- with torch.no_grad():
- inputs, target = inputs.to(args.device), target.to(args.device)
- # compute output from model
- output = model(inputs)
-
- if not args.earlyexit_thresholds:
- # compute loss
- loss = criterion(output, target)
- # measure accuracy and record loss
- losses['objective_loss'].add(loss.item())
- classerr.add(output.data, target)
- if args.display_confusion:
- confusion.add(output.data, target)
- else:
- earlyexit_validate_loss(output, target, criterion, args)
-
- # measure elapsed time
- batch_time.add(time.time() - end)
- end = time.time()
-
- steps_completed = (validation_step+1)
- if steps_completed % args.print_freq == 0:
- if not args.earlyexit_thresholds:
- stats = ('',
- OrderedDict([('Loss', losses['objective_loss'].mean),
- ('Top1', classerr.value(1)),
- ('Top5', classerr.value(5))]))
- else:
- stats_dict = OrderedDict()
- stats_dict['Test'] = validation_step
- for exitnum in range(args.num_exits):
- la_string = 'LossAvg' + str(exitnum)
- stats_dict[la_string] = args.losses_exits[exitnum].mean
- # Because of the nature of ClassErrorMeter, if an exit is never taken during the batch,
- # then accessing the value(k) will cause a divide by zero. So we'll build the OrderedDict
- # accordingly and we will not print for an exit error when that exit is never taken.
- if args.exit_taken[exitnum]:
- t1 = 'Top1_exit' + str(exitnum)
- t5 = 'Top5_exit' + str(exitnum)
- stats_dict[t1] = args.exiterrors[exitnum].value(1)
- stats_dict[t5] = args.exiterrors[exitnum].value(5)
- stats = ('Performance/Validation/', stats_dict)
-
- distiller.log_training_progress(stats, None, epoch, steps_completed,
- total_steps, args.print_freq, loggers)
+def early_exit_init(args):
if not args.earlyexit_thresholds:
- msglogger.info('==> Top1: %.3f Top5: %.3f Loss: %.3f\n',
- classerr.value()[0], classerr.value()[1], losses['objective_loss'].mean)
-
- if args.display_confusion:
- msglogger.info('==> Confusion:\n%s\n', str(confusion.value()))
- return classerr.value(1), classerr.value(5), losses['objective_loss'].mean
- else:
- total_top1, total_top5, losses_exits_stats = earlyexit_validate_stats(args)
- return total_top1, total_top5, losses_exits_stats[args.num_exits-1]
-
-
-def update_training_scores_history(perf_scores_history, model, top1, top5, epoch, num_best_scores):
- """ Update the list of top training scores achieved so far, and log the best scores so far"""
-
- model_sparsity, _, params_nnz_cnt = distiller.model_params_stats(model)
- perf_scores_history.append(distiller.MutableNamedTuple({'params_nnz_cnt': -params_nnz_cnt,
- 'sparsity': model_sparsity,
- 'top1': top1, 'top5': top5, 'epoch': epoch}))
- # Keep perf_scores_history sorted from best to worst
- # Sort by sparsity as main sort key, then sort by top1, top5 and epoch
- perf_scores_history.sort(key=operator.attrgetter('params_nnz_cnt', 'top1', 'top5', 'epoch'), reverse=True)
- for score in perf_scores_history[:num_best_scores]:
- msglogger.info('==> Best [Top1: %.3f Top5: %.3f Sparsity:%.2f Params: %d on epoch: %d]',
- score.top1, score.top5, score.sparsity, -score.params_nnz_cnt, score.epoch)
-
-
-def earlyexit_loss(output, target, criterion, args):
- loss = 0
- sum_lossweights = 0
- for exitnum in range(args.num_exits-1):
- loss += (args.earlyexit_lossweights[exitnum] * criterion(output[exitnum], target))
- sum_lossweights += args.earlyexit_lossweights[exitnum]
- args.exiterrors[exitnum].add(output[exitnum].data, target)
- # handle final exit
- loss += (1.0 - sum_lossweights) * criterion(output[args.num_exits-1], target)
- args.exiterrors[args.num_exits-1].add(output[args.num_exits-1].data, target)
- return loss
-
-
-def earlyexit_validate_loss(output, target, criterion, args):
- # We need to go through each sample in the batch itself - in other words, we are
- # not doing batch processing for exit criteria - we do this as though it were batchsize of 1
- # but with a grouping of samples equal to the batch size.
- # Note that final group might not be a full batch - so determine actual size.
- this_batch_size = target.size()[0]
- earlyexit_validate_criterion = nn.CrossEntropyLoss(reduce=False).to(args.device)
-
- for exitnum in range(args.num_exits):
- # calculate losses at each sample separately in the minibatch.
- args.loss_exits[exitnum] = earlyexit_validate_criterion(output[exitnum], target)
- # for batch_size > 1, we need to reduce this down to an average over the batch
- args.losses_exits[exitnum].add(torch.mean(args.loss_exits[exitnum]).cpu())
-
- for batch_index in range(this_batch_size):
- earlyexit_taken = False
- # take the exit using CrossEntropyLoss as confidence measure (lower is more confident)
- for exitnum in range(args.num_exits - 1):
- if args.loss_exits[exitnum][batch_index] < args.earlyexit_thresholds[exitnum]:
- # take the results from early exit since lower than threshold
- args.exiterrors[exitnum].add(torch.tensor(np.array(output[exitnum].data[batch_index].cpu(), ndmin=2)),
- torch.full([1], target[batch_index], dtype=torch.long))
- args.exit_taken[exitnum] += 1
- earlyexit_taken = True
- break # since exit was taken, do not affect the stats of subsequent exits
- # this sample does not exit early and therefore continues until final exit
- if not earlyexit_taken:
- exitnum = args.num_exits - 1
- args.exiterrors[exitnum].add(torch.tensor(np.array(output[exitnum].data[batch_index].cpu(), ndmin=2)),
- torch.full([1], target[batch_index], dtype=torch.long))
- args.exit_taken[exitnum] += 1
-
-
-def earlyexit_validate_stats(args):
- # Print some interesting summary stats for number of data points that could exit early
- top1k_stats = [0] * args.num_exits
- top5k_stats = [0] * args.num_exits
- losses_exits_stats = [0] * args.num_exits
- sum_exit_stats = 0
- for exitnum in range(args.num_exits):
- if args.exit_taken[exitnum]:
- sum_exit_stats += args.exit_taken[exitnum]
- msglogger.info("Exit %d: %d", exitnum, args.exit_taken[exitnum])
- top1k_stats[exitnum] += args.exiterrors[exitnum].value(1)
- top5k_stats[exitnum] += args.exiterrors[exitnum].value(5)
- losses_exits_stats[exitnum] += args.losses_exits[exitnum].mean
- for exitnum in range(args.num_exits):
- if args.exit_taken[exitnum]:
- msglogger.info("Percent Early Exit %d: %.3f", exitnum,
- (args.exit_taken[exitnum]*100.0) / sum_exit_stats)
- total_top1 = 0
- total_top5 = 0
- for exitnum in range(args.num_exits):
- total_top1 += (top1k_stats[exitnum] * (args.exit_taken[exitnum] / sum_exit_stats))
- total_top5 += (top5k_stats[exitnum] * (args.exit_taken[exitnum] / sum_exit_stats))
- msglogger.info("Accuracy Stats for exit %d: top1 = %.3f, top5 = %.3f", exitnum, top1k_stats[exitnum], top5k_stats[exitnum])
- msglogger.info("Totals for entire network with early exits: top1 = %.3f, top5 = %.3f", total_top1, total_top5)
- return total_top1, total_top5, losses_exits_stats
-
-
-def evaluate_model(model, criterion, test_loader, loggers, activations_collectors, args, scheduler=None):
- # This sample application can be invoked to evaluate the accuracy of your model on
- # the test dataset.
- # You can optionally quantize the model to 8-bit integer before evaluation.
- # For example:
- # python3 compress_classifier.py --arch resnet20_cifar ../data.cifar10 -p=50 --resume-from=checkpoint.pth.tar --evaluate
-
- if not isinstance(loggers, list):
- loggers = [loggers]
+ return
+ args.num_exits = len(args.earlyexit_thresholds) + 1
+ args.loss_exits = [0] * args.num_exits
+ args.losses_exits = []
+ args.exiterrors = []
+ msglogger.info('=> using early-exit threshold values of %s', args.earlyexit_thresholds)
- if args.quantize_eval:
- model.cpu()
- quantizer = quantization.PostTrainLinearQuantizer.from_args(model, args)
- quantizer.prepare_model(distiller.get_dummy_input(input_shape=model.input_shape))
- model.to(args.device)
- top1, _, _ = test(test_loader, model, criterion, loggers, activations_collectors, args=args)
+class ClassifierCompressorSampleApp(classifier.ClassifierCompressor):
+ def __init__(self, args, script_dir):
+ super().__init__(args, script_dir)
+ early_exit_init(args)
- if args.quantize_eval:
- checkpoint_name = 'quantized'
- apputils.save_checkpoint(0, args.arch, model, optimizer=None, scheduler=scheduler,
- name='_'.join([args.name, checkpoint_name]) if args.name else checkpoint_name,
- dir=msglogger.logdir, extras={'quantized_top1': top1})
+ def handle_subapps(self):
+ return handle_subapps(self.model, self.criterion, self.optimizer,
+ self.compression_scheduler, self.pylogger, self.args)
def sensitivity_analysis(model, criterion, data_loader, loggers, args, sparsities):
@@ -657,9 +173,9 @@ def sensitivity_analysis(model, criterion, data_loader, loggers, args, sparsitie
msglogger.info("Running sensitivity tests")
if not isinstance(loggers, list):
loggers = [loggers]
- test_fnc = partial(test, test_loader=data_loader, criterion=criterion,
+ test_fnc = partial(classifier.test, test_loader=data_loader, criterion=criterion,
loggers=loggers, args=args,
- activations_collectors=create_activation_stats_collectors(model))
+ activations_collectors=classifier.create_activation_stats_collectors(model))
which_params = [param_name for param_name, _ in model.named_parameters()]
sensitivity = distiller.perform_sensitivity_analysis(model,
net_params=which_params,
@@ -670,26 +186,12 @@ def sensitivity_analysis(model, criterion, data_loader, loggers, args, sparsitie
distiller.sensitivities_to_csv(sensitivity, os.path.join(msglogger.logdir, 'sensitivity.csv'))
-def automated_deep_compression(model, criterion, optimizer, loggers, args):
- train_loader, val_loader, test_loader, _ = load_data(args)
-
- args.display_confusion = True
- validate_fn = partial(test, test_loader=test_loader, criterion=criterion,
- loggers=loggers, args=args, activations_collectors=None)
- train_fn = partial(train, train_loader=train_loader, criterion=criterion,
- loggers=loggers, args=args)
-
- save_checkpoint_fn = partial(apputils.save_checkpoint, arch=args.arch, dir=msglogger.logdir)
- optimizer_data = {'lr': args.lr, 'momentum': args.momentum, 'weight_decay': args.weight_decay}
- adc.do_adc(model, args, optimizer_data, validate_fn, save_checkpoint_fn, train_fn)
-
-
def greedy(model, criterion, optimizer, loggers, args):
- train_loader, val_loader, test_loader, _ = load_data(args)
+ train_loader, val_loader, test_loader = classifier.load_data(args)
- test_fn = partial(test, test_loader=test_loader, criterion=criterion,
+ test_fn = partial(classifier.test, test_loader=test_loader, criterion=criterion,
loggers=loggers, args=args, activations_collectors=None)
- train_fn = partial(train, train_loader=train_loader, criterion=criterion, args=args)
+ train_fn = partial(classifier.train, train_loader=train_loader, criterion=criterion, args=args)
assert args.greedy_target_density is not None
distiller.pruning.greedy_filter_pruning.greedy_pruner(model, args,
args.greedy_target_density,
@@ -697,83 +199,6 @@ def greedy(model, criterion, optimizer, loggers, args):
test_fn, train_fn)
-def acts_quant_stats_collection(model, criterion, loggers, args):
- msglogger.info('Collecting quantization calibration stats based on {:.1%} of test dataset'
- .format(args.qe_calibration))
- model = distiller.utils.make_non_parallel_copy(model)
- args.effective_test_size = args.qe_calibration
- train_loader, val_loader, test_loader, _ = load_data(args)
- test_fn = partial(test, test_loader=test_loader, criterion=criterion,
- loggers=loggers, args=args, activations_collectors=None)
- collect_quant_stats(model, test_fn, save_dir=msglogger.logdir,
- classes=None, inplace_runtime_check=True, disable_inplace_attrs=True)
-
-
-def acts_histogram_collection(model, criterion, loggers, args):
- msglogger.info('Collecting activation histograms based on {:.1%} of test dataset'
- .format(args.activation_histograms))
- model = distiller.utils.make_non_parallel_copy(model)
- args.effective_test_size = args.activation_histograms
- train_loader, val_loader, test_loader, _ = load_data(args, fixed_subset=True)
- test_fn = partial(test, test_loader=test_loader, criterion=criterion,
- loggers=loggers, args=args, activations_collectors=None)
- collect_histograms(model, test_fn, save_dir=msglogger.logdir,
- classes=None, nbins=2048, save_hist_imgs=True)
-
-
-def load_data(args, fixed_subset=False):
- return apputils.load_data(args.dataset, os.path.expanduser(args.data), args.batch_size,
- args.workers, args.validation_split, args.deterministic,
- args.effective_train_size, args.effective_valid_size, args.effective_test_size,
- fixed_subset)
-
-
-class missingdict(dict):
- """This is a little trick to prevent KeyError"""
- def __missing__(self, key):
- return None # note, does *not* set self[key] - we don't want defaultdict's behavior
-
-
-def create_activation_stats_collectors(model, *phases):
- """Create objects that collect activation statistics.
-
- This is a utility function that creates two collectors:
- 1. Fine-grade sparsity levels of the activations
- 2. L1-magnitude of each of the activation channels
-
- Args:
- model - the model on which we want to collect statistics
- phases - the statistics collection phases: train, valid, and/or test
-
- WARNING! Enabling activation statsitics collection will significantly slow down training!
- """
- genCollectors = lambda: missingdict({
- "sparsity": SummaryActivationStatsCollector(model, "sparsity",
- lambda t: 100 * distiller.utils.sparsity(t)),
- "l1_channels": SummaryActivationStatsCollector(model, "l1_channels",
- distiller.utils.activation_channels_l1),
- "apoz_channels": SummaryActivationStatsCollector(model, "apoz_channels",
- distiller.utils.activation_channels_apoz),
- "mean_channels": SummaryActivationStatsCollector(model, "mean_channels",
- distiller.utils.activation_channels_means),
- "records": RecordsActivationStatsCollector(model, classes=[torch.nn.Conv2d])
- })
-
- return {k: (genCollectors() if k in phases else missingdict())
- for k in ('train', 'valid', 'test')}
-
-
-def save_collectors_data(collectors, directory):
- """Utility function that saves all activation statistics to disk.
-
- File type and format of contents are collector-specific.
- """
- for name, collector in collectors.items():
- msglogger.info('Saving data for collector {}...'.format(name))
- file_path = collector.save(os.path.join(directory, name))
- msglogger.info("Saved to {}".format(file_path))
-
-
if __name__ == '__main__':
try:
main()
@@ -791,6 +216,6 @@ if __name__ == '__main__':
msglogger.handlers = handlers_bak
raise
finally:
- if msglogger is not None:
+ if msglogger is not None and hasattr(msglogger, 'log_filename'):
msglogger.info('')
msglogger.info('Log file for this run: ' + os.path.realpath(msglogger.log_filename))
diff --git a/examples/classifier_compression/multi-run.py b/examples/classifier_compression/multi-run.py
new file mode 100644
index 0000000..1fd1f9f
--- /dev/null
+++ b/examples/classifier_compression/multi-run.py
@@ -0,0 +1,66 @@
+#
+# Copyright (c) 2019 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""A simple script to run several instances of the same experiment configuration, in parallel.
+
+The script will launch multiple processes (equal to the number of GPUs on the machine).
+Each process from execute one instance of the experiment command.
+
+Format:
+ $ python multi-run.py <experiment output directory> <experiment command-line>
+
+ Each experiment uses a different (single) GPU instance, uses a different seed and is assigned an ID in the
+ range [0..number-of-gpus].
+ All experiments log directories are nested under the main output directory: <experiment output directory>
+
+ <experiment command-line> conforms to the command-line interface of compress_classifier.py when
+ compress_classifier.py is the application you want to execute. You can also execute other applications that
+ derive from distiller.apputils.ClassifierCompressor.
+
+
+Example:
+ $ python multi-run.py experiments/plain20-random-l1_rank compress_classifier.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=l1-rank --amc-agent-algo=Random-policy --amc-cfg=../automated_deep_compression/auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=random -j=1
+
+"""
+
+import time
+import os
+import sys
+import torch
+import argparse
+from multiprocessing import Pool
+
+def run_experiment(args):
+ experiment_outdir, id, seed, gpu = args
+ experiment_cmd = " ".join(sys.argv[2:])
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--seed', type=int, default=1000,
+ help='seed the PRNG for CPU, CUDA, numpy, and Python')
+ #experiment_cmd += + ' > /dev/null'
+ multi_args, unknown = parser.parse_known_args()
+ os.system("python3 {} --name={} --gpus={} --seed={} --out-dir={}".format(experiment_cmd, id, gpu,
+ multi_args.seed+id, experiment_outdir))
+
+# Create the directory that will store the outputs of all the executions.
+timestr = time.strftime("%Y.%m.%d-%H%M%S")
+experiment_outdir = os.path.join(sys.argv[1], timestr)
+os.makedirs(experiment_outdir, exist_ok=False)
+
+n_gpus = torch.cuda.device_count()
+pool = Pool(processes=n_gpus)
+pool.map_async(run_experiment, [(experiment_outdir, id, 1000+id, id%n_gpus) for id in range(n_gpus)])
+pool.close()
+pool.join()
\ No newline at end of file
diff --git a/examples/classifier_compression/parser.py b/examples/classifier_compression/parser.py
index 38df6e8..cad734b 100755
--- a/examples/classifier_compression/parser.py
+++ b/examples/classifier_compression/parser.py
@@ -15,130 +15,16 @@
#
import argparse
-
import distiller
import distiller.quantization
-import examples.automated_deep_compression as adc
-from distiller.utils import float_range_argparse_checker as float_range
import distiller.models as models
-SUMMARY_CHOICES = ['sparsity', 'compute', 'model', 'modules', 'png', 'png_w_params']
-
-
-def get_parser():
- parser = argparse.ArgumentParser(description='Distiller image classification model compression')
- parser.add_argument('data', metavar='DIR', help='path to dataset')
- parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18', type=lambda s: s.lower(),
- choices=models.ALL_MODEL_NAMES,
- help='model architecture: ' +
- ' | '.join(models.ALL_MODEL_NAMES) +
- ' (default: resnet18)')
- parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
- help='number of data loading workers (default: 4)')
- parser.add_argument('--epochs', type=int, metavar='N',
- help='number of total epochs to run (default: 90')
- parser.add_argument('-b', '--batch-size', default=256, type=int,
- metavar='N', help='mini-batch size (default: 256)')
-
- optimizer_args = parser.add_argument_group('Optimizer arguments')
- optimizer_args.add_argument('--lr', '--learning-rate', default=0.1,
- type=float, metavar='LR', help='initial learning rate')
- optimizer_args.add_argument('--momentum', default=0.9, type=float,
- metavar='M', help='momentum')
- optimizer_args.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
- metavar='W', help='weight decay (default: 1e-4)')
-
- parser.add_argument('--print-freq', '-p', default=10, type=int,
- metavar='N', help='print frequency (default: 10)')
- parser.add_argument('--verbose', '-v', action='store_true', help='Emit debug log messages')
-
- load_checkpoint_group = parser.add_argument_group('Resuming arguments')
- load_checkpoint_group_exc = load_checkpoint_group.add_mutually_exclusive_group()
- # TODO(barrh): args.deprecated_resume is deprecated since v0.3.1
- load_checkpoint_group_exc.add_argument('--resume', dest='deprecated_resume', default='', type=str,
- metavar='PATH', help=argparse.SUPPRESS)
- load_checkpoint_group_exc.add_argument('--resume-from', dest='resumed_checkpoint_path', default='',
- type=str, metavar='PATH',
- help='path to latest checkpoint. Use to resume paused training session.')
- load_checkpoint_group_exc.add_argument('--exp-load-weights-from', dest='load_model_path',
- default='', type=str, metavar='PATH',
- help='path to checkpoint to load weights from (excluding other fields) (experimental)')
- load_checkpoint_group.add_argument('--pretrained', dest='pretrained', action='store_true',
- help='use pre-trained model')
- load_checkpoint_group.add_argument('--reset-optimizer', action='store_true',
- help='Flag to override optimizer if resumed from checkpoint. This will reset epochs count.')
-
- parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
- help='evaluate model on test set')
- parser.add_argument('--activation-stats', '--act-stats', nargs='+', metavar='PHASE', default=list(),
- help='collect activation statistics on phases: train, valid, and/or test'
- ' (WARNING: this slows down training)')
- parser.add_argument('--activation-histograms', '--act-hist',
- type=distiller.utils.float_range_argparse_checker(exc_min=True),
- metavar='PORTION_OF_TEST_SET',
- help='Run the model in evaluation mode on the specified portion of the test dataset and '
- 'generate activation histograms. NOTE: This slows down evaluation significantly')
- parser.add_argument('--masks-sparsity', dest='masks_sparsity', action='store_true', default=False,
- help='print masks sparsity table at end of each epoch')
- parser.add_argument('--param-hist', dest='log_params_histograms', action='store_true', default=False,
- help='log the parameter tensors histograms to file '
- '(WARNING: this can use significant disk space)')
- parser.add_argument('--summary', type=lambda s: s.lower(), choices=SUMMARY_CHOICES, action='append',
- help='print a summary of the model, and exit - options: | '.join(SUMMARY_CHOICES))
- parser.add_argument('--export-onnx', action='store', nargs='?', type=str, const='model.onnx', default=None,
- help='export model to ONNX format')
- parser.add_argument('--compress', dest='compress', type=str, nargs='?', action='store',
- help='configuration file for pruning the model (default is to use hard-coded schedule)')
- parser.add_argument('--sense', dest='sensitivity', choices=['element', 'filter', 'channel'],
- type=lambda s: s.lower(), help='test the sensitivity of layers to pruning')
- parser.add_argument('--sense-range', dest='sensitivity_range', type=float, nargs=3, default=[0.0, 0.95, 0.05],
- help='an optional parameter for sensitivity testing '
- 'providing the range of sparsities to test.\n'
- 'This is equivalent to creating sensitivities = np.arange(start, stop, step)')
- parser.add_argument('--extras', default=None, type=str,
- help='file with extra configuration information')
- parser.add_argument('--deterministic', '--det', action='store_true',
- help='Ensure deterministic execution for re-producible results.')
- parser.add_argument('--seed', type=int, default=None,
- help='seed the PRNG for CPU, CUDA, numpy, and Python')
- parser.add_argument('--gpus', metavar='DEV_ID', default=None,
- help='Comma-separated list of GPU device IDs to be used '
- '(default is to use all available devices)')
- parser.add_argument('--cpu', action='store_true', default=False,
- help='Use CPU only. \n'
- 'Flag not set => uses GPUs according to the --gpus flag value.'
- 'Flag set => overrides the --gpus flag')
- parser.add_argument('--name', '-n', metavar='NAME', default=None, help='Experiment name')
- parser.add_argument('--out-dir', '-o', dest='output_dir', default='logs', help='Path to dump logs and checkpoints')
- parser.add_argument('--validation-split', '--valid-size', '--vs', dest='validation_split',
- type=float_range(exc_max=True), default=0.1,
- help='Portion of training dataset to set aside for validation')
- parser.add_argument('--effective-train-size', '--etrs', type=float_range(exc_min=True), default=1.,
- help='Portion of training dataset to be used in each epoch. '
- 'NOTE: If --validation-split is set, then the value of this argument is applied '
- 'AFTER the train-validation split according to that argument')
- parser.add_argument('--effective-valid-size', '--evs', type=float_range(exc_min=True), default=1.,
- help='Portion of validation dataset to be used in each epoch. '
- 'NOTE: If --validation-split is set, then the value of this argument is applied '
- 'AFTER the train-validation split according to that argument')
- parser.add_argument('--effective-test-size', '--etes', type=float_range(exc_min=True), default=1.,
- help='Portion of test dataset to be used in each epoch')
- parser.add_argument('--confusion', dest='display_confusion', default=False, action='store_true',
- help='Display the confusion matrix')
+def add_cmdline_args(parser):
parser.add_argument('--earlyexit_lossweights', type=float, nargs='*', dest='earlyexit_lossweights', default=None,
help='List of loss weights for early exits (e.g. --earlyexit_lossweights 0.1 0.3)')
parser.add_argument('--earlyexit_thresholds', type=float, nargs='*', dest='earlyexit_thresholds', default=None,
help='List of EarlyExit thresholds (e.g. --earlyexit_thresholds 1.2 0.9)')
- parser.add_argument('--num-best-scores', dest='num_best_scores', default=1, type=int,
- help='number of best scores to track and report (default: 1)')
- parser.add_argument('--load-serialized', dest='load_serialized', action='store_true', default=False,
- help='Load a model without DataParallel wrapping it')
- parser.add_argument('--thinnify', dest='thinnify', action='store_true', default=False,
- help='physically remove zero-filters and create a smaller model')
-
distiller.knowledge_distillation.add_distillation_args(parser, models.ALL_MODEL_NAMES, True)
- distiller.quantization.add_post_train_quant_args(parser)
distiller.pruning.greedy_filter_pruning.add_greedy_pruner_args(parser)
- adc.automl_args.add_automl_args(parser)
return parser
diff --git a/examples/ssl/resnet20_cifar_baseline_training.yaml b/examples/ssl/resnet20_cifar_baseline_training.yaml
index e2f113a..5561b81 100755
--- a/examples/ssl/resnet20_cifar_baseline_training.yaml
+++ b/examples/ssl/resnet20_cifar_baseline_training.yaml
@@ -2,6 +2,10 @@
#
# time python3 compress_classifier.py --arch resnet20_cifar ../data.cifar10 -p=50 --lr=0.3 --epochs=180 --compress=../ssl/resnet20_cifar_baseline_training.yaml -j=1 --deterministic
#
+# We can acheive faster and better training with a more complicated training regim which yields a
+# Top1~ 91.7 - 92.0 on the test set.
+# For backwards compatibility with the results of older examples, the new training schedule is left in remark.
+#
# Parameters:
# +----+-------------------------------------+----------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
# | | Name | Shape | NNZ (dense) | NNZ (sparse) | Cols (%) | Rows (%) | Ch (%) | 2D (%) | 3D (%) | Fine (%) | Std | Mean | Abs-Mean |
@@ -60,3 +64,21 @@ policies:
starting_epoch: 45
ending_epoch: 200
frequency: 1
+
+# This is the faster training, referred to above.
+# Use this command line:
+#
+# time python3 compress_classifier.py --arch resnet20_cifar ../data.cifar10 -p=50 --epochs=110 --compress=../ssl/resnet20_cifar_baseline_training.yaml --vs=0 --gpus=0 -j=4 --lr=0.4
+#
+# lr_schedulers:
+# training_lr:
+# class: CosineAnnealingLR
+# T_max: 20
+
+
+# policies:
+# - lr_scheduler:
+# instance_name: training_lr
+# starting_epoch: 0
+# ending_epoch: 100
+# frequency: 1
diff --git a/examples/util_scripts/multi-run.py b/examples/util_scripts/multi-run.py
new file mode 100755
index 0000000..1696500
--- /dev/null
+++ b/examples/util_scripts/multi-run.py
@@ -0,0 +1,49 @@
+#
+# Copyright (c) 2019 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""A script to run several instances of the same experiment configuration.
+
+Format:
+ $ python multi-run.py <experiment output directory> <experiment command-line>
+
+Example:
+ $ python multi-run.py experiments/plain20-random-l1_rank compress_classifier.py --arch=plain20_cifar ${CIFAR10_PATH} --resume=checkpoint.plain20_cifar.pth.tar --lr=0.05 --amc --amc-protocol=mac-constrained --amc-action-range 0.05 1.0 --amc-target-density=0.5 -p=50 --etes=0.075 --amc-ft-epochs=0 --amc-prune-pattern=channels --amc-prune-method=l1-rank --amc-agent-algo=Random-policy --amc-cfg=../automated_deep_compression/auto_compression_channels.yaml --evs=0.5 --etrs=0.5 --amc-rllib=random -j=1
+
+"""
+
+import time
+import os
+import sys
+import torch
+from multiprocessing import Pool
+
+def run_experiment(args):
+ experiment_outdir, id, seed, gpu = args
+ experiment_cmd = " ".join(sys.argv[2:])
+ #experiment_cmd += + ' > /dev/null'
+ os.system("python3 {} --name={} --gpus={} --seed={} --out-dir={}".format(experiment_cmd, id,
+ gpu, seed, experiment_outdir))
+
+# Create the directory that will store the outputs of all the executions.
+timestr = time.strftime("%Y.%m.%d-%H%M%S")
+experiment_outdir = os.path.join(sys.argv[1], timestr)
+os.makedirs(experiment_outdir, exist_ok=False)
+
+n_gpus = torch.cuda.device_count()
+pool = Pool(processes=n_gpus)
+pool.map_async(run_experiment, [(experiment_outdir, id, 1000+id, id%n_gpus) for id in range(n_gpus)])
+pool.close()
+pool.join()
\ No newline at end of file
diff --git a/tests/full_flow_tests.py b/tests/full_flow_tests.py
index b7944bf..20d19df 100755
--- a/tests/full_flow_tests.py
+++ b/tests/full_flow_tests.py
@@ -125,7 +125,7 @@ test_configs = [
DS_CIFAR, accuracy_checker, [44.370, 89.640]),
TestConfig('-a resnet20_cifar --resume {0} --sense=filter --sense-range 0 0.10 0.05'.
format(os.path.join(examples_root, 'ssl', 'checkpoints', 'checkpoint_trained_dense.pth.tar')),
- DS_CIFAR, collateral_checker, [('sensitivity.csv', 3175), ('sensitivity.png', 96158)])
+ DS_CIFAR, collateral_checker, [('sensitivity.csv', 3175), ('sensitivity.png', 96157)])
]
diff --git a/tests/test_pruning.py b/tests/test_pruning.py
index e5d8635..277fa95 100755
--- a/tests/test_pruning.py
+++ b/tests/test_pruning.py
@@ -16,6 +16,7 @@
from collections import namedtuple
import numpy as np
import logging
+import math
import torch
import distiller
import common
@@ -120,16 +121,20 @@ def test_ranked_filter_pruning(parallel):
is_parallel=parallel)
test_vgg19_conv_fc_interface(parallel, model=model, zeros_mask_dict=zeros_mask_dict)
-
+# todo: add a similar test for ranked channel pruning
def test_prune_all_filters(parallel):
"""Pruning all of the filteres in a weights tensor of a Convolution
is illegal and should raise an exception.
"""
with pytest.raises(ValueError):
- ranked_filter_pruning(resnet20_cifar(parallel), ratio_to_prune=1.0, is_parallel=parallel)
+ ranked_filter_pruning(resnet20_cifar(parallel), ratio_to_prune=1.0,
+ is_parallel=parallel, rounding_fn=math.ceil)
+ with pytest.raises(ValueError):
+ ranked_filter_pruning(resnet20_cifar(parallel), ratio_to_prune=1.0,
+ is_parallel=parallel, rounding_fn=math.floor)
-def ranked_filter_pruning(config, ratio_to_prune, is_parallel):
+def ranked_filter_pruning(config, ratio_to_prune, is_parallel, rounding_fn=math.floor):
"""Test L1 ranking and pruning of filters.
First we rank and prune the filters of a Convolutional layer using
a L1RankedStructureParameterPruner. Then we physically remove the
@@ -154,7 +159,8 @@ def ranked_filter_pruning(config, ratio_to_prune, is_parallel):
pruner = distiller.pruning.L1RankedStructureParameterPruner("filter_pruner",
group_type="Filters",
desired_sparsity=ratio_to_prune,
- weights=pair[0] + ".weight")
+ weights=pair[0] + ".weight",
+ rounding_fn=rounding_fn)
pruner.set_param_mask(conv1_p, pair[0] + ".weight", zeros_mask_dict, meta=None)
conv1 = common.find_module_by_name(model, pair[0])
--
GitLab