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Itaicaspi/episode reset refactoring (#105)

Browse Source 
* reordering of the episode reset operation and allowing to store episodes only when they are terminated

* reordering of the episode reset operation and allowing to store episodes only when they are terminated

* revert tensorflow-gpu to 1.9.0 + bug fix in should_train()

* tests readme file and refactoring of policy optimization agent train function

* Update README.md

* Update README.md

* additional policy optimization train function simplifications

* Updated the traces after the reordering of the environment reset

* docker and jenkins files

* updated the traces to the ones from within the docker container

* updated traces and added control suite to the docker

* updated jenkins file with the intel proxy + updated doom basic a3c test params

* updated line breaks in jenkins file

* added a missing line break in jenkins file

* refining trace tests ignored presets + adding a configurable beta entropy value

* switch the order of trace and golden tests in jenkins + fix golden tests processes not killed issue

* updated benchmarks for dueling ddqn breakout and pong

* allowing dynamic updates to the loss weights + bug fix in episode.update_returns

* remove docker and jenkins file
This commit is contained in:
Itai Caspi
2018-09-04 15:07:54 +03:00
committed by GitHub
parent 7086492127
commit 72a1d9d426
92 changed files with 9803 additions and 9740 deletions
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313
rl_coach/traces/Atari_A3C_LSTM_breakout/trace.csv
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@@ -40,162 +40,157 @@ Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,To
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182,326.0,0.0,123.0,1.0,123.0,5581.0,0.0,2.0,2.0,0.0,,,,,,,,,,,,0.2708955,0.2947027,0.9294537,0.12768297,1.3718787000000001,0.003369857,1.3861516,1.3687766000000001,0.027819253504276283,0.2701356709606049,0.9372680187225342,-0.11355036497116087,0.6112494,0.0151050715,0.6398752,0.5917235,0.0707294,0.11288732,0.32045668,0.011123533,0.040826526,0.24641089,0.58862627,-0.084971346
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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 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
40 39 0.0 1.0 24.0 1.0 24.0 986.0 0.0 0.0
41 40 0.0 1.0 24.0 1.0 24.0 1010.0 0.0 0.0
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