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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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198
rl_coach/traces/Fetch_DDPG_HER_baselines_reach/trace.csv
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@@ -20,102 +20,102 @@ Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,To
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114,3720.0,0.0,28044.0,114.0,50.0,5700.0,0.1,-9.0,-9.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.17013283,0.37420806,0.0417809,-1.0723688999999998,,,,,-0.06900360784895289,0.5460522710945572,1.019577215316937,-1.3968503986441518
115,3760.0,0.0,28290.0,115.0,50.0,5750.0,0.1,-4.0,-4.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.06579996,0.29076207,0.058597103,-1.4247187,,,,,-0.004493505795523075,0.6136554163702884,1.472823580373972,-1.2416349537344498
116,3800.0,0.0,28536.0,116.0,50.0,5800.0,0.1,-8.0,-8.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.16179584,0.35272563,0.040722996000000004,-1.1867014999999999,,,,,0.060401610943115996,0.4892185843214472,1.2113853893719546,-1.1761648386468997
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118,3880.0,0.0,29028.0,118.0,50.0,5900.0,0.1,-8.0,-8.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.15213348,0.3317145,0.020325545,-1.0353725,,,,,-0.05101856436570349,0.5387009379599047,1.3515704991835902,-1.5471903533129372
119,3920.0,0.0,29274.0,119.0,50.0,5950.0,0.1,-11.0,-11.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.28316557,0.63153785,0.034552652,-2.4969523,,,,,0.15632540383201854,0.5998432157452396,1.2668380149528908,-1.4226570735217936
120,3960.0,0.0,29520.0,120.0,50.0,6000.0,0.1,-14.0,-14.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.66879606,1.2630769,0.057835735,-4.0341687,,,,,0.020119700310501143,0.5829865887817238,1.289173587063609,-1.2461655323473746
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 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
20 19 0.0 1.0 4674.0 19.0 50.0 950.0 0.1 0.0
21 20 0.0 1.0 4920.0 20.0 50.0 1000.0 0.1 0.0
22 21 0.0 1.0 5166.0 21.0 50.0 1050.0 0.1 1.0
23 22 40.0 0.0 5412.0 22.0 50.0 1100.0 0.1 -50.0 -46.0 -50.0 -46.0 1.0 5.207162576168775 5.272886256501079 2.2367193101900678 2.2031638400757174 8.568733185529709 8.5393485724926 0.7174199819564819 0.7541730403900146 0.001 0.0 0.001 0.001 0.56313926 0.5317002 0.5711164000000001 0.5374275 2.7792726 2.6782672 0.10739021 0.12463605 -0.62783873 -0.40694425 0.24770412 0.116917714 -0.0020706265 0.022069091000000002 -1.1272142 -0.730958 -0.7256970775194886 -0.6921883965816761 0.4294583896943829 0.4142855914450438 0.0 -1.1396800428628922 -1.0443030893802645 0.06503581008285231 0.021984492637073118 0.3822671138978558 0.3201371912853629 0.9814501104952102 -0.9401749419176604 -0.9372007403745176
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