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Running trace tests in parallel + other small fixes

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This commit is contained in:
Shadi Endrawis
2018-08-30 19:34:52 +03:00
parent ebe574e463
commit 07db625987
5 changed files with 254 additions and 299 deletions
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2
benchmarks/a3c/README.md
View File

@@ -1,7 +1,7 @@
# A3C
Each experiment uses 3 seeds.
The parameters used for Clipped PPO are the same parameters as described in the [original paper](https://arxiv.org/abs/1707.06347).
The parameters used for A3C are the same parameters as described in the [original paper](https://arxiv.org/abs/1602.01783).
### Inverted Pendulum A3C - 1/2/4/8/16 workers

11
rl_coach/presets/Doom_Basic_DFP.py
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@@ -1,5 +1,5 @@
from rl_coach.agents.dfp_agent import DFPAgentParameters, HandlingTargetsAfterEpisodeEnd
from rl_coach.base_parameters import VisualizationParameters
from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
from rl_coach.environments.doom_environment import DoomEnvironmentParameters
from rl_coach.environments.environment import SelectedPhaseOnlyDumpMethod, MaxDumpMethod
@@ -46,5 +46,12 @@ vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.trace_max_env_steps = 2000
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=vis_params)
schedule_params=schedule_params, vis_params=vis_params,
preset_validation_params=preset_validation_params)

113
rl_coach/tests/golden_tests.py
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@@ -44,12 +44,6 @@ def read_csv_paths(test_path, filename_pattern, read_csv_tries=100):
return csv_paths
def clean_df(df):
if 'Wall-Clock Time' in df.keys():
df.drop(['Wall-Clock Time'], 1, inplace=True)
return df
def print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, args):
percentage = int((100 * last_num_episodes) / preset_validation_params.max_episodes_to_achieve_reward)
sys.stdout.write("\rReward: ({}/{})".format(round(averaged_rewards[-1], 1),
@@ -186,94 +180,8 @@ def perform_reward_based_tests(args, preset_validation_params, preset_name):
return test_passed
def perform_trace_based_tests(args, preset_name, num_env_steps, level=None):
test_name = '__test_trace'
test_path = os.path.join('./experiments', test_name)
if path.exists(test_path):
shutil.rmtree(test_path)
# run the experiment in a separate thread
screen.log_title("Running test {}{}".format(preset_name, ' - ' + level if level else ''))
log_file_name = 'test_log_{preset_name}.txt'.format(preset_name=preset_name)
cmd = (
'python3 rl_coach/coach.py '
'-p {preset_name} '
'-e {test_name} '
'--seed 42 '
'-c '
'--no_summary '
'-cp {custom_param} '
'{level} '
'&> {log_file_name} '
).format(
preset_name=preset_name,
test_name=test_name,
log_file_name=log_file_name,
level='-lvl ' + level if level else '',
custom_param='\"improve_steps=EnvironmentSteps({n});'
'steps_between_evaluation_periods=EnvironmentSteps({n});'
'evaluation_steps=EnvironmentSteps(1);'
'heatup_steps=EnvironmentSteps(1024)\"'.format(n=num_env_steps)
)
p = subprocess.Popen(cmd, shell=True, executable="/bin/bash", preexec_fn=os.setsid)
p.wait()
filename_pattern = '*.csv'
# get the csv with the results
csv_paths = read_csv_paths(test_path, filename_pattern)
test_passed = False
if not csv_paths:
screen.error("csv file never found", crash=False)
if args.verbose:
screen.error("command exitcode: {}".format(p.returncode), crash=False)
screen.error(open(log_file_name).read(), crash=False)
else:
trace_path = os.path.join('./rl_coach', 'traces', preset_name + '_' + level.replace(':', '_') if level else preset_name, '')
if not os.path.exists(trace_path):
screen.log('No trace found, creating new trace in: {}'.format(trace_path))
os.makedirs(os.path.dirname(trace_path))
df = pd.read_csv(csv_paths[0])
df = clean_df(df)
df.to_csv(os.path.join(trace_path, 'trace.csv'), index=False)
screen.success("Successfully created new trace.")
test_passed = True
else:
test_df = pd.read_csv(csv_paths[0])
test_df = clean_df(test_df)
new_trace_csv_path = os.path.join(trace_path, 'trace_new.csv')
test_df.to_csv(new_trace_csv_path, index=False)
test_df = pd.read_csv(new_trace_csv_path)
trace_csv_path = glob.glob(path.join(trace_path, 'trace.csv'))
trace_csv_path = trace_csv_path[0]
trace_df = pd.read_csv(trace_csv_path)
test_passed = test_df.equals(trace_df)
if test_passed:
screen.success("Passed successfully.")
os.remove(new_trace_csv_path)
test_passed = True
else:
screen.error("Trace test failed.", crash=False)
if args.overwrite:
os.remove(trace_csv_path)
os.rename(new_trace_csv_path, trace_csv_path)
screen.error("Overwriting old trace.", crash=False)
else:
screen.error("bcompare {} {}".format(trace_csv_path, new_trace_csv_path), crash=False)
shutil.rmtree(test_path)
os.remove(log_file_name)
return test_passed
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-t', '--trace',
help="(flag) perform trace based testing",
action='store_true')
parser.add_argument('-p', '--preset',
help="(string) Name of a preset to run (as configured in presets.py)",
default=None,
@@ -295,15 +203,11 @@ def main():
parser.add_argument('-np', '--no_progress_bar',
help="(flag) Don't print the progress bar (makes jenkins logs more readable)",
action='store_true')
parser.add_argument('-ow', '--overwrite',
help="(flag) overwrite old trace with new ones in trace testing mode",
action='store_true')
args = parser.parse_args()
if args.preset is not None:
presets_lists = [args.preset]
else:
# presets_lists = list_all_classes_in_module(presets)
presets_lists = [f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets')) if
f[-3:] == '.py' and not f == '__init__.py']
@@ -329,23 +233,10 @@ def main():
continue
preset_validation_params = preset.graph_manager.preset_validation_params
if not args.trace and not preset_validation_params.test:
if not preset_validation_params.test:
continue
if args.trace:
num_env_steps = preset_validation_params.trace_max_env_steps
if preset_validation_params.trace_test_levels:
for level in preset_validation_params.trace_test_levels:
test_count += 1
test_passed = perform_trace_based_tests(args, preset_name, num_env_steps, level)
if not test_passed:
fail_count += 1
else:
test_count += 1
test_passed = perform_trace_based_tests(args, preset_name, num_env_steps)
if not test_passed:
fail_count += 1
else:
test_passed = perform_reward_based_tests(args, preset_validation_params, preset_name)
if not test_passed:
fail_count += 1
@@ -358,6 +249,4 @@ def main():
if __name__ == '__main__':
os.environ['DISABLE_MUJOCO_RENDERING'] = '1'
main()
del os.environ['DISABLE_MUJOCO_RENDERING']

239
rl_coach/tests/trace_tests.py Normal file
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@@ -0,0 +1,239 @@
#
# Copyright (c) 2017 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 argparse
import glob
import os
import shutil
import subprocess
import multiprocessing
import sys
from importlib import import_module
from os import path
sys.path.append('.')
import pandas as pd
import time
# -*- coding: utf-8 -*-
from rl_coach.logger import screen
def read_csv_paths(test_path, filename_pattern, read_csv_tries=100):
csv_paths = []
tries_counter = 0
while not csv_paths:
csv_paths = glob.glob(path.join(test_path, '*', filename_pattern))
if tries_counter > read_csv_tries:
break
tries_counter += 1
time.sleep(1)
return csv_paths
def clean_df(df):
if 'Wall-Clock Time' in df.keys():
df.drop(['Wall-Clock Time'], 1, inplace=True)
return df
def run_trace_based_test(preset_name, num_env_steps, level=None):
test_name = '__test_trace_{}{}'.format(preset_name, '_' + level if level else '').replace(':', '_')
test_path = os.path.join('./experiments', test_name)
if path.exists(test_path):
shutil.rmtree(test_path)
# run the experiment in a separate thread
screen.log_title("Running test {}{}".format(preset_name, ' - ' + level if level else ''))
log_file_name = 'test_log_{preset_name}.txt'.format(preset_name=test_name[13:])
cmd = (
'python3 rl_coach/coach.py '
'-p {preset_name} '
'-e {test_name} '
'--seed 42 '
'-c '
'--no_summary '
'-cp {custom_param} '
'{level} '
'&> {log_file_name} '
).format(
preset_name=preset_name,
test_name=test_name,
log_file_name=log_file_name,
level='-lvl ' + level if level else '',
custom_param='\"improve_steps=EnvironmentSteps({n});'
'steps_between_evaluation_periods=EnvironmentSteps({n});'
'evaluation_steps=EnvironmentSteps(1);'
'heatup_steps=EnvironmentSteps(1024)\"'.format(n=num_env_steps)
)
p = subprocess.Popen(cmd, shell=True, executable="/bin/bash", preexec_fn=os.setsid)
return test_path, log_file_name, p
def wait_and_check(args, processes, force=False):
if not force and len(processes) < args.max_threads:
return None
test_path = processes[0][0]
test_name = test_path.split('/')[-1]
log_file_name = processes[0][1]
p = processes[0][2]
processes.pop(0)
p.wait()
filename_pattern = '*.csv'
# get the csv with the results
csv_paths = read_csv_paths(test_path, filename_pattern)
test_passed = False
screen.log('Results for {}: '.format(test_name[13:]))
if not csv_paths:
screen.error("csv file never found", crash=False)
if args.verbose:
screen.error("command exitcode: {}".format(p.returncode), crash=False)
screen.error(open(log_file_name).read(), crash=False)
else:
trace_path = os.path.join('./rl_coach', 'traces', test_name[13:])
if not os.path.exists(trace_path):
screen.log('No trace found, creating new trace in: {}'.format(trace_path))
os.makedirs(os.path.dirname(trace_path))
df = pd.read_csv(csv_paths[0])
df = clean_df(df)
df.to_csv(os.path.join(trace_path, 'trace.csv'), index=False)
screen.success("Successfully created new trace.")
test_passed = True
else:
test_df = pd.read_csv(csv_paths[0])
test_df = clean_df(test_df)
new_trace_csv_path = os.path.join(trace_path, 'trace_new.csv')
test_df.to_csv(new_trace_csv_path, index=False)
test_df = pd.read_csv(new_trace_csv_path)
trace_csv_path = glob.glob(path.join(trace_path, 'trace.csv'))
trace_csv_path = trace_csv_path[0]
trace_df = pd.read_csv(trace_csv_path)
test_passed = test_df.equals(trace_df)
if test_passed:
screen.success("Passed successfully.")
os.remove(new_trace_csv_path)
test_passed = True
else:
screen.error("Trace test failed.", crash=False)
if args.overwrite:
os.remove(trace_csv_path)
os.rename(new_trace_csv_path, trace_csv_path)
screen.error("Overwriting old trace.", crash=False)
else:
screen.error("bcompare {} {}".format(trace_csv_path, new_trace_csv_path), crash=False)
shutil.rmtree(test_path)
os.remove(log_file_name)
return test_passed
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--preset',
help="(string) Name of a preset to run (as configured in presets.py)",
default=None,
type=str)
parser.add_argument('-ip', '--ignore_presets',
help="(string) Name of a preset(s) to ignore (comma separated, and as configured in presets.py)",
default=None,
type=str)
parser.add_argument('-v', '--verbose',
help="(flag) display verbose logs in the event of an error",
action='store_true')
parser.add_argument('--stop_after_first_failure',
help="(flag) stop executing tests after the first error",
action='store_true')
parser.add_argument('-ow', '--overwrite',
help="(flag) overwrite old trace with new ones in trace testing mode",
action='store_true')
parser.add_argument('-prl', '--parallel',
help="(flag) run tests in parallel",
action='store_true')
parser.add_argument('-mt', '--max_threads',
help="(string) Name of a preset(s) to ignore (comma separated, and as configured in presets.py)",
default=multiprocessing.cpu_count()-2,
type=int)
args = parser.parse_args()
if not args.parallel:
args.max_threads = 1
if args.preset is not None:
presets_lists = [args.preset]
else:
presets_lists = [f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets')) if
f[-3:] == '.py' and not f == '__init__.py']
fail_count = 0
test_count = 0
processes = []
if args.ignore_presets is not None:
presets_to_ignore = args.ignore_presets.split(',')
else:
presets_to_ignore = []
for idx, preset_name in enumerate(sorted(presets_lists)):
if args.stop_after_first_failure and fail_count > 0:
break
if preset_name not in presets_to_ignore:
try:
preset = import_module('rl_coach.presets.{}'.format(preset_name))
except:
screen.error("Failed to load preset <{}>".format(preset_name), crash=False)
fail_count += 1
test_count += 1
continue
preset_validation_params = preset.graph_manager.preset_validation_params
num_env_steps = preset_validation_params.trace_max_env_steps
if preset_validation_params.trace_test_levels:
for level in preset_validation_params.trace_test_levels:
test_count += 1
test_path, log_file, p = run_trace_based_test(preset_name, num_env_steps, level)
processes.append((test_path, log_file, p))
test_passed = wait_and_check(args, processes)
if test_passed is not None and not test_passed:
fail_count += 1
else:
test_count += 1
test_path, log_file, p = run_trace_based_test(preset_name, num_env_steps)
processes.append((test_path, log_file, p))
test_passed = wait_and_check(args, processes)
if test_passed is not None and not test_passed:
fail_count += 1
while len(processes) > 0:
test_passed = wait_and_check(args, processes, force=True)
if test_passed is not None and not test_passed:
fail_count += 1
screen.separator()
if fail_count == 0:
screen.success(" Summary: " + str(test_count) + "/" + str(test_count) + " tests passed successfully")
else:
screen.error(" Summary: " + str(test_count - fail_count) + "/" + str(test_count) + " tests passed successfully")
if __name__ == '__main__':
os.environ['DISABLE_MUJOCO_RENDERING'] = '1'
main()
del os.environ['DISABLE_MUJOCO_RENDERING']

180
rl_coach/traces/Doom_Basic_DFP/trace.csv
View File

@@ -64,183 +64,3 @@ Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,To
63,1755.0,0.0,2814.0,63.0,41.0,2814.0,0.4122000000000097,-86.0,-86.0,0.0,,,,1949.386736613948,438.01420887891226,2898.2509765625,1162.8082275390625,0.0001,0.0,0.0001,0.0001,8248.635,5983.8467,27771.922000000002,1338.8822
64,1766.0,0.0,2825.0,64.0,11.0,2825.0,0.4116500000000097,47.0,47.0,0.0,,,,2142.1350430575285,338.90633193526367,2704.8408203125,1582.5853271484375,0.0001,0.0,0.0001,0.0001,6870.5347,3548.6636,14772.337,2794.1462
65,1841.0,0.0,2900.0,65.0,75.0,2900.0,0.4079000000000101,-345.0,-345.0,0.0,,,,2000.6128336588545,430.99328281858436,2908.90380859375,960.3004150390624,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,10136.796,5358.4326,28253.752,1138.26
66,1916.0,0.0,2975.0,66.0,75.0,2975.0,0.40415000000001056,-335.0,-335.0,0.0,,,,2026.6920833333331,529.8637039992993,3619.700927734375,1070.4171142578123,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,8430.834,5336.768,30607.21,1764.9081
67,1951.0,0.0,3010.0,67.0,35.0,3010.0,0.4024000000000108,-67.0,-67.0,0.0,,,,1841.2743495396203,523.0806394453233,3080.92529296875,975.4871826171876,0.0001,1.3552527156068802e-20,0.0001,0.0001,10914.858999999999,5581.9185,25406.693,2296.1365
68,2026.0,0.0,3085.0,68.0,75.0,3085.0,0.3986500000000112,-340.0,-340.0,0.0,,,,1931.1769523111982,484.47308677205046,3420.0205078125,980.9301147460938,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,8593.916,4831.423,19849.654,1627.7633
69,2101.0,0.0,3160.0,69.0,75.0,3160.0,0.3949000000000116,-345.0,-345.0,0.0,,,,1923.9647298177085,439.42036009674086,3085.549072265625,1138.4078369140625,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,8419.5205,5118.085999999999,25471.596,1676.1218
70,2104.0,0.0,3163.0,70.0,3.0,3163.0,0.3947500000000116,91.0,91.0,0.0,,,,1963.133748372396,177.5880000863903,2142.828125,1721.3369140625,0.0001,0.0,0.0001,0.0001,11553.153,6762.5264,20039.734,3491.342
71,2179.0,0.0,3238.0,71.0,75.0,3238.0,0.391000000000012,-340.0,-340.0,0.0,,,,1881.029920247396,472.85035611123817,3133.014404296875,743.2744750976562,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,8786.094000000001,4887.0664,24731.27,1773.8262
72,2244.0,0.0,3303.0,72.0,65.0,3303.0,0.3877500000000124,-177.0,-177.0,0.0,,,,1903.962170410156,499.99813041775946,3514.205322265625,939.2587280273438,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,10310.058,5624.4259999999995,22822.584,915.6345
73,2319.0,0.0,3378.0,73.0,75.0,3378.0,0.3840000000000128,-350.0,-350.0,0.0,,,,1944.437877604167,495.72390861489606,3659.293212890625,1183.776611328125,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,10649.211000000001,6002.4717,26810.895,1770.1194
74,2321.0,0.0,3380.0,74.0,2.0,3380.0,0.3839000000000128,95.0,95.0,0.0,,,,1570.7232055664062,95.63555908203124,1666.3587646484375,1475.087646484375,0.0001,0.0,0.0001,0.0001,21296.816000000003,1725.0479,23021.865,19571.77
75,2396.0,0.0,3455.0,75.0,75.0,3455.0,0.3801500000000132,-330.0,-330.0,0.0,,,,1810.9820288085934,446.48992755328214,3309.293701171875,851.1087036132812,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,9836.215,5952.2515,34327.64,1166.5629
76,2404.0,0.0,3463.0,76.0,8.0,3463.0,0.3797500000000133,66.0,66.0,0.0,,,,1722.9540252685547,268.4618902885067,2165.13134765625,1357.0504150390625,0.0001,0.0,0.0001,0.0001,9979.93,5023.6777,19294.695,3491.3025
77,2479.0,0.0,3538.0,77.0,75.0,3538.0,0.3760000000000137,-335.0,-335.0,0.0,,,,1782.0905338541668,494.2559075195012,2989.8828125,882.3601684570312,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,9387.821,6066.056,29493.766,1117.8618
78,2554.0,0.0,3613.0,78.0,75.0,3613.0,0.3722500000000141,-355.0,-355.0,0.0,,,,1793.683448893229,427.59461326554526,2930.960693359375,985.106689453125,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,9373.855,6267.786,28627.327999999998,1318.4742
79,2629.0,0.0,3688.0,79.0,75.0,3688.0,0.3685000000000145,-325.0,-325.0,0.0,,,,1727.9209236653644,428.69109990951307,2856.00048828125,900.2325439453125,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,8225.533000000001,4055.9172,18916.656000000003,1403.0117
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204,4575.0,0.0,5634.0,204.0,4.0,5634.0,0.2712000000000252,87.0,87.0,0.0,,,,1454.028076171875,53.97420827627877,1542.7210693359375,1396.605712890625,0.0001,0.0,0.0001,0.0001,9496.091,3327.8552,14472.473999999998,5527.3037
205,4578.0,0.0,5637.0,205.0,3.0,5637.0,0.2710500000000252,91.0,91.0,0.0,,,,1195.4170532226562,248.3491423848797,1546.6256103515625,1017.498291015625,0.0001,0.0,0.0001,0.0001,5456.2007,978.7539999999999,6504.8813,4149.465999999999
206,4590.0,0.0,5649.0,206.0,12.0,5649.0,0.2704500000000253,50.0,50.0,0.0,,,,1502.4513244628906,266.70748814951696,1830.2315673828125,1098.6324462890623,0.0001,0.0,0.0001,0.0001,8429.972,5128.5693,20201.748,4278.0566
207,4596.0,0.0,5655.0,207.0,6.0,5655.0,0.2701500000000253,79.0,79.0,0.0,,,,1430.7368977864585,202.5043291403373,1681.7098388671875,1074.0697021484377,0.0001,0.0,0.0001,0.0001,8954.146,3551.645,14961.569,3283.2056
208,4610.0,0.0,5669.0,208.0,14.0,5669.0,0.2694500000000254,40.0,40.0,0.0,,,,1652.7965000697545,356.1602918315661,2163.094482421875,1081.569580078125,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,10869.048999999999,4849.6140000000005,20559.186,5289.6064
209,4615.0,0.0,5674.0,209.0,5.0,5674.0,0.2692000000000254,83.0,83.0,0.0,,,,1681.4314697265625,322.42301489480025,2098.826904296875,1266.2509765625,0.0001,0.0,0.0001,0.0001,7572.9453,2217.3032,11037.6875,4694.7466
210,4617.0,0.0,5676.0,210.0,2.0,5676.0,0.26910000000002543,95.0,95.0,0.0,,,,1348.9115600585938,272.27325439453125,1621.184814453125,1076.6383056640623,0.0001,0.0,0.0001,0.0001,7164.2427,2647.395,9811.638,4516.8477
211,4619.0,0.0,5678.0,211.0,2.0,5678.0,0.26900000000002544,95.0,95.0,0.0,,,,1769.4951782226562,15.71759033203125,1785.2127685546875,1753.777587890625,0.0001,0.0,0.0001,0.0001,10710.598999999998,5013.0464,15723.645,5697.5522
212,4623.0,0.0,5682.0,212.0,4.0,5682.0,0.26880000000002546,87.0,87.0,0.0,,,,1666.2909851074219,413.76646768779887,2176.74853515625,1074.2369384765623,0.0001,0.0,0.0001,0.0001,10705.37,1991.9036,12927.566,7481.0740000000005
213,4654.0,0.0,5713.0,213.0,31.0,5713.0,0.26725000000002563,-42.0,-42.0,0.0,,,,1517.6040649414062,276.5834880959856,2049.357421875,1016.9600219726562,9.999999999999996e-05,4.0657581468206416e-20,0.0001,0.0001,8801.716,3137.5347,18404.82,3577.5085
214,4660.0,0.0,5719.0,214.0,6.0,5719.0,0.2669500000000257,79.0,79.0,0.0,,,,1523.5877482096357,177.5030720002979,1898.7828369140625,1379.9332275390625,0.0001,0.0,0.0001,0.0001,7898.236999999999,4535.226,14576.226999999999,3499.3977
215,4667.0,0.0,5726.0,215.0,7.0,5726.0,0.2666000000000257,71.0,71.0,0.0,,,,1645.5178397042414,289.8183943048827,2057.625732421875,1186.8642578125,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,8452.396,4916.943,18332.613,4052.1826
216,4692.0,0.0,5751.0,216.0,25.0,5751.0,0.26535000000002584,-6.0,-6.0,0.0,,,,1540.976630859375,258.37371284451945,2062.619384765625,1006.59765625,0.0001,0.0,0.0001,0.0001,9024.776,5126.067,24159.225,3195.883
217,4698.0,0.0,5757.0,217.0,6.0,5757.0,0.2650500000000259,79.0,79.0,0.0,,,,1479.3026529947915,216.0859805190166,1746.343017578125,1097.7117919921877,0.0001,0.0,0.0001,0.0001,5758.19,2055.9333,9747.193000000001,3750.2075
218,4711.0,0.0,5770.0,218.0,13.0,5770.0,0.26440000000002595,51.0,51.0,0.0,,,,1773.6507004957932,361.62397387439654,2573.411865234375,1238.7900390625,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,6949.6606,1816.8661,10488.733999999999,4925.685
219,4721.0,0.0,5780.0,219.0,10.0,5780.0,0.263900000000026,63.0,63.0,0.0,,,,1586.0868774414062,328.0589972396793,2141.182373046875,1187.7752685546875,0.0001,0.0,0.0001,0.0001,9106.851,5068.215,21763.125,3262.1459999999997
220,4726.0,0.0,5785.0,220.0,5.0,5785.0,0.26365000000002603,83.0,83.0,0.0,,,,1680.9486572265625,327.58172455078545,1994.9149169921875,1242.515869140625,0.0001,0.0,0.0001,0.0001,7719.1235,3869.813,15213.167,4619.1216
221,4736.0,0.0,5795.0,221.0,10.0,5795.0,0.2631500000000261,58.0,58.0,0.0,,,,1635.6147094726562,231.12813882830457,2128.69482421875,1304.0673828125,0.0001,0.0,0.0001,0.0001,8095.1494,3601.9033,16742.742,4002.5303
222,4738.0,0.0,5797.0,222.0,2.0,5797.0,0.2630500000000261,95.0,95.0,0.0,,,,1830.385498046875,149.6759033203125,1980.0614013671875,1680.7095947265625,0.0001,0.0,0.0001,0.0001,7536.210999999999,3354.2083,10890.419,4182.0024
223,4744.0,0.0,5803.0,223.0,6.0,5803.0,0.2627500000000261,72.0,72.0,0.0,,,,1464.723958333333,275.06768383884065,1992.8956298828125,1111.5037841796877,0.0001,0.0,0.0001,0.0001,8623.386999999999,4957.4487,15606.116000000002,2728.4656
224,4750.0,0.0,5809.0,224.0,6.0,5809.0,0.26245000000002616,79.0,79.0,0.0,,,,1615.9028930664062,311.1717063009196,2176.079345703125,1272.09228515625,0.0001,0.0,0.0001,0.0001,11878.875,4581.366,17811.021,5136.9414
225,4765.0,0.0,5824.0,225.0,15.0,5824.0,0.26170000000002624,36.0,36.0,0.0,,,,1672.9819091796876,210.88478575899475,2030.317138671875,1290.590576171875,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,10036.551,5322.6140000000005,20271.004,2782.7222
226,4780.0,0.0,5839.0,226.0,15.0,5839.0,0.2609500000000263,38.0,38.0,0.0,,,,1535.6995035807295,210.80740111818818,1924.0511474609373,1180.5965576171875,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,9226.402,4697.968,21891.441000000003,3777.8591
227,4789.0,0.0,5848.0,227.0,9.0,5848.0,0.2605000000000264,67.0,67.0,0.0,,,,1646.0743204752607,409.7565667239258,2175.978759765625,1011.2977905273438,0.0001,0.0,0.0001,0.0001,10136.023000000001,3781.2566,16120.398000000001,6237.516
228,4818.0,0.0,5877.0,228.0,29.0,5877.0,0.2590500000000265,-35.0,-35.0,0.0,,,,1559.3227665342133,273.9418668446787,2049.352294921875,987.2787475585938,9.999999999999998e-05,2.7105054312137605e-20,0.0001,0.0001,9085.735,4919.093,23523.92,2891.33
229,4828.0,0.0,5887.0,229.0,10.0,5887.0,0.2585500000000266,51.0,51.0,0.0,,,,1506.9610595703125,215.6564052660068,2003.4285888671875,1281.3740234375,0.0001,0.0,0.0001,0.0001,10262.436,4546.483,18351.277,4846.438
230,4837.0,0.0,5896.0,230.0,9.0,5896.0,0.25810000000002664,67.0,67.0,0.0,,,,1676.7249891493054,277.7869741646706,2231.30517578125,1365.53173828125,0.0001,0.0,0.0001,0.0001,8322.197,3037.5327,12989.811000000002,2565.16
231,4848.0,0.0,5907.0,231.0,11.0,5907.0,0.2575500000000267,55.0,55.0,0.0,,,,1570.4157159978693,197.23745977427987,1825.733642578125,1227.6495361328125,0.0001,0.0,0.0001,0.0001,8234.487,4947.722,20699.697,2746.322
232,4854.0,0.0,5913.0,232.0,6.0,5913.0,0.25725000000002674,79.0,79.0,0.0,,,,1372.9749145507812,287.7551878558951,2012.5904541015625,1210.560302734375,0.0001,0.0,0.0001,0.0001,6266.3784,2125.4338,9649.707,3190.8259999999996
233,4860.0,0.0,5919.0,233.0,6.0,5919.0,0.2569500000000268,79.0,79.0,0.0,,,,1618.4681396484375,295.9615978535304,1983.81787109375,1131.422119140625,0.0001,0.0,0.0001,0.0001,10951.181,4456.469,19153.105,5207.122
234,4873.0,0.0,5932.0,234.0,13.0,5932.0,0.25630000000002684,51.0,51.0,0.0,,,,1698.3113356370193,446.1104348049165,2588.162353515625,1185.9578857421875,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,9447.996,4557.3906,18626.89,3016.4314
235,4877.0,0.0,5936.0,235.0,4.0,5936.0,0.2561000000000269,87.0,87.0,0.0,,,,1547.6767883300781,122.0488317128621,1681.738525390625,1352.802978515625,0.0001,0.0,0.0001,0.0001,4932.94,558.6947,5699.3745,4283.1187
236,4889.0,0.0,5948.0,236.0,12.0,5948.0,0.2555000000000269,55.0,55.0,0.0,,,,1745.5220031738281,266.12821400981085,2212.082763671875,1260.177978515625,0.0001,0.0,0.0001,0.0001,11376.927,5885.085999999999,21552.43,3054.1272
237,4895.0,0.0,5954.0,237.0,6.0,5954.0,0.25520000000002696,79.0,79.0,0.0,,,,1629.5961710611982,324.6063949133215,2059.597900390625,1139.7100830078125,0.0001,0.0,0.0001,0.0001,9038.09,3959.1523,16073.103000000001,5262.1123
238,4900.0,0.0,5959.0,238.0,5.0,5959.0,0.254950000000027,83.0,83.0,0.0,,,,1537.5740478515625,235.65928396964625,1967.217529296875,1283.4539794921875,0.0001,0.0,0.0001,0.0001,9189.04,5576.1416,19855.607,4272.5435
239,4906.0,0.0,5965.0,239.0,6.0,5965.0,0.25465000000002697,72.0,72.0,0.0,,,,1672.2214965820312,251.86218177116032,2063.068603515625,1296.59619140625,0.0001,0.0,0.0001,0.0001,9695.2295,5264.0019999999995,20286.506,4498.5884
240,4909.0,0.0,5968.0,240.0,3.0,5968.0,0.25450000000002704,91.0,91.0,0.0,,,,1471.9488525390625,86.28899252027995,1584.484619140625,1374.8096923828125,0.0001,0.0,0.0001,0.0001,7936.315,2075.2244,10782.089,5892.13
241,4915.0,0.0,5974.0,241.0,6.0,5974.0,0.2542000000000271,72.0,72.0,0.0,,,,1557.1255493164062,299.23584550953615,2135.84375,1210.2655029296875,0.0001,0.0,0.0001,0.0001,6037.5664,1908.9031,9879.327,4138.912
242,4939.0,0.0,5998.0,242.0,24.0,5998.0,0.2530000000000272,-8.0,-8.0,0.0,,,,1589.8535817464194,338.7673203226463,2664.65478515625,1135.1231689453125,0.0001,0.0,0.0001,0.0001,7549.754,2949.2222,14405.286,3644.848
243,4975.0,0.0,6034.0,243.0,36.0,6034.0,0.2512000000000274,-60.0,-60.0,0.0,,,,1640.5213911268447,346.9037830915075,2444.94287109375,982.4774780273438,0.0001,1.3552527156068802e-20,0.0001,0.0001,10391.169,5203.11,22767.365,3682.6194
244,4985.0,0.0,6044.0,244.0,10.0,6044.0,0.25070000000002746,63.0,63.0,0.0,,,,1640.57412109375,305.4435223452175,2161.6591796875,1069.760986328125,0.0001,0.0,0.0001,0.0001,9709.154,3455.7705,13697.825,4402.343
245,4993.0,0.0,6052.0,245.0,8.0,6052.0,0.2503000000000275,64.0,64.0,0.0,,,,1622.1454620361328,326.6483500890629,2149.472412109375,1184.996826171875,0.0001,0.0,0.0001,0.0001,10903.11,5498.238,21330.82,4377.806
1 Episode # Training Iter In Heatup ER #Transitions ER #Episodes Episode Length Total steps Epsilon Shaped Training Reward Training Reward Update Target Network 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
64 63 1755.0 0.0 2814.0 63.0 41.0 2814.0 0.4122000000000097 -86.0 -86.0 0.0 1949.386736613948 438.01420887891226 2898.2509765625 1162.8082275390625 0.0001 0.0 0.0001 0.0001 8248.635 5983.8467 27771.922000000002 1338.8822
65 64 1766.0 0.0 2825.0 64.0 11.0 2825.0 0.4116500000000097 47.0 47.0 0.0 2142.1350430575285 338.90633193526367 2704.8408203125 1582.5853271484375 0.0001 0.0 0.0001 0.0001 6870.5347 3548.6636 14772.337 2794.1462
66 65 1841.0 0.0 2900.0 65.0 75.0 2900.0 0.4079000000000101 -345.0 -345.0 0.0 2000.6128336588545 430.99328281858436 2908.90380859375 960.3004150390624 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 10136.796 5358.4326 28253.752 1138.26
66 1916.0 0.0 2975.0 66.0 75.0 2975.0 0.40415000000001056 -335.0 -335.0 0.0 2026.6920833333331 529.8637039992993 3619.700927734375 1070.4171142578123 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 8430.834 5336.768 30607.21 1764.9081
67 1951.0 0.0 3010.0 67.0 35.0 3010.0 0.4024000000000108 -67.0 -67.0 0.0 1841.2743495396203 523.0806394453233 3080.92529296875 975.4871826171876 0.0001 1.3552527156068802e-20 0.0001 0.0001 10914.858999999999 5581.9185 25406.693 2296.1365
68 2026.0 0.0 3085.0 68.0 75.0 3085.0 0.3986500000000112 -340.0 -340.0 0.0 1931.1769523111982 484.47308677205046 3420.0205078125 980.9301147460938 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 8593.916 4831.423 19849.654 1627.7633
69 2101.0 0.0 3160.0 69.0 75.0 3160.0 0.3949000000000116 -345.0 -345.0 0.0 1923.9647298177085 439.42036009674086 3085.549072265625 1138.4078369140625 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 8419.5205 5118.085999999999 25471.596 1676.1218
70 2104.0 0.0 3163.0 70.0 3.0 3163.0 0.3947500000000116 91.0 91.0 0.0 1963.133748372396 177.5880000863903 2142.828125 1721.3369140625 0.0001 0.0 0.0001 0.0001 11553.153 6762.5264 20039.734 3491.342
71 2179.0 0.0 3238.0 71.0 75.0 3238.0 0.391000000000012 -340.0 -340.0 0.0 1881.029920247396 472.85035611123817 3133.014404296875 743.2744750976562 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 8786.094000000001 4887.0664 24731.27 1773.8262
72 2244.0 0.0 3303.0 72.0 65.0 3303.0 0.3877500000000124 -177.0 -177.0 0.0 1903.962170410156 499.99813041775946 3514.205322265625 939.2587280273438 0.00010000000000000002 1.3552527156068802e-20 0.0001 0.0001 10310.058 5624.4259999999995 22822.584 915.6345
73 2319.0 0.0 3378.0 73.0 75.0 3378.0 0.3840000000000128 -350.0 -350.0 0.0 1944.437877604167 495.72390861489606 3659.293212890625 1183.776611328125 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 10649.211000000001 6002.4717 26810.895 1770.1194
74 2321.0 0.0 3380.0 74.0 2.0 3380.0 0.3839000000000128 95.0 95.0 0.0 1570.7232055664062 95.63555908203124 1666.3587646484375 1475.087646484375 0.0001 0.0 0.0001 0.0001 21296.816000000003 1725.0479 23021.865 19571.77
75 2396.0 0.0 3455.0 75.0 75.0 3455.0 0.3801500000000132 -330.0 -330.0 0.0 1810.9820288085934 446.48992755328214 3309.293701171875 851.1087036132812 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 9836.215 5952.2515 34327.64 1166.5629
76 2404.0 0.0 3463.0 76.0 8.0 3463.0 0.3797500000000133 66.0 66.0 0.0 1722.9540252685547 268.4618902885067 2165.13134765625 1357.0504150390625 0.0001 0.0 0.0001 0.0001 9979.93 5023.6777 19294.695 3491.3025
77 2479.0 0.0 3538.0 77.0 75.0 3538.0 0.3760000000000137 -335.0 -335.0 0.0 1782.0905338541668 494.2559075195012 2989.8828125 882.3601684570312 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 9387.821 6066.056 29493.766 1117.8618
78 2554.0 0.0 3613.0 78.0 75.0 3613.0 0.3722500000000141 -355.0 -355.0 0.0 1793.683448893229 427.59461326554526 2930.960693359375 985.106689453125 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 9373.855 6267.786 28627.327999999998 1318.4742
79 2629.0 0.0 3688.0 79.0 75.0 3688.0 0.3685000000000145 -325.0 -325.0 0.0 1727.9209236653644 428.69109990951307 2856.00048828125 900.2325439453125 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 8225.533000000001 4055.9172 18916.656000000003 1403.0117
80 2631.0 0.0 3690.0 80.0 2.0 3690.0 0.3684000000000145 95.0 95.0 0.0 1715.1917724609375 501.3409423828125 2216.53271484375 1213.850830078125 0.0001 0.0 0.0001 0.0001 8337.032 1406.0388 9743.071 6930.9937
81 2706.0 0.0 3765.0 81.0 75.0 3765.0 0.3646500000000149 -335.0 -335.0 0.0 1693.4950537109376 368.2939104993159 2532.328369140625 984.902099609375 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 7530.8843 4220.2344 21542.822 1399.1561
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