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Itaicaspi/episode reset refactoring (#105)

* 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

View File

@@ -1,26 +1,31 @@
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,Q/Mean,Q/Stdev,Q/Max,Q/Min
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3,0.0,1.0,642.0,642.0,111.0,642.0,7.0,,,0.0,,,,,,,,,,,,,,,,,,,
4,0.0,1.0,910.0,910.0,268.0,910.0,5.0,,,0.0,,,,,,,,,,,,,,,,,,,
5,0.0,1.0,1073.0,1073.0,163.0,1073.0,1.0,,,0.0,,,,,,,,,,,,,,,,,,,
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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 Q/Mean Q/Stdev Q/Max Q/Min
2 1 0.0 1.0 269.0 269.0 269.0 269.0 7.0 0.0
3 2 0.0 1.0 531.0 531.0 262.0 531.0 3.0 8.0 0.0
4 3 0.0 1.0 642.0 654.0 642.0 654.0 111.0 123.0 642.0 654.0 7.0 0.0 0.0
5 4 0.0 1.0 910.0 1173.0 910.0 1173.0 268.0 519.0 910.0 1173.0 5.0 2.0 0.0
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9 8 119.0 229.0 0.0 1550.0 2090.0 1550.0 2090.0 200.0 176.0 1550.0 2090.0 5.0 7.0 2.0 45.0 15.0 0.0 0.007876217254815856 0.00864216006702072 0.009940253725264405 0.013155937701958298 0.031399693340063095 0.061171818524599075 0.00010039866901934148 0.00018360336252953854 0.00025 0.0 0.00025 0.00025 0.005710288399999999 0.0063004736 0.005156745999999999 0.0053449036 0.020595245 0.02487476 0.0013811021 0.0019890803
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13 12 385.0 334.0 0.0 2611.0 2508.0 2611.0 2508.0 170.0 78.0 2611.0 2508.0 1.0 0.0 3.0 2.0 30.0 45.0 0.0 0.005404766050010356 0.009793104943941887 0.008490796312006945 0.011249256480204521 0.030357897281646732 0.030716722831130024 0.00013974733883515 0.0001681474968791008 0.00025 0.0002500000000000001 0.0 5.421010862427521e-20 0.00025 0.00025 0.0044133128 0.006330653 0.0039297505 0.0047096196 0.013841972 0.014098721000000002 0.0012999868 0.0016581904999999999
14 13 456.0 378.0 0.0 2898.0 2684.0 2898.0 2684.0 287.0 176.0 2898.0 2684.0 6.0 5.0 5.0 0.0 90.0 0.0 0.0 0.004022052818597754 0.01011617302819187 0.007086255561643891 0.013193202268065932 0.030808655545115467 0.04607892408967018 0.00014122753054834902 0.00014670072414446622 0.00025 0.0 0.00025 0.00025 0.0038387226 0.0067802290000000005 0.0034813196000000003 0.005344481 0.013518038999999999 0.022279864 0.0014702336999999998 0.0016253429999999998 0.027286835 0.0073864055 0.03329719 0.016612418
15 14 497.0 425.0 0.0 3062.0 2872.0 3062.0 2872.0 164.0 188.0 3062.0 2872.0 4.0 0.0 0.0 0.0 0.0 0.0042548857908979885 0.00757620267153896 0.0074758880380001455 0.010181129044846313 0.03025482408702373 0.03055970929563045 0.00016087367839645594 0.00019950376008637252 0.00025 0.0002500000000000001 0.0 5.421010862427521e-20 0.00025 0.00025 0.0039765667 0.005839123000000001 0.0035351904 0.004426101 0.013480025 0.01861591 0.0016169035 0.0021889664000000002
16 15 617.0 449.0 0.0 3542.0 2967.0 3542.0 2967.0 480.0 95.0 3542.0 2967.0 1.0 6.0 6.0 3.0 75.0 30.0 0.0 0.006593966874910014 0.0070000348653896545 0.009290417878413549 0.011226164764985254 0.030973667278885838 0.04456117376685143 0.000136182876303792 0.00021107358043082056 0.0002500000000000001 0.00025 1.0842021724855042e-19 0.0 0.00025 0.00025 0.004996340300000001 0.005659159 0.004391069 0.0046324306 0.02013114 0.021839907000000002 0.001432074 0.0021402768 0.014001195 0.0048342915 0.020594118 0.0064951440000000004
17 16 749.0 469.0 0.0 4069.0 3049.0 4069.0 3049.0 527.0 82.0 4069.0 3049.0 9.0 3.0 14.0 0.0 245.0 0.0 0.0 0.008309065473440569 0.012940272329433357 0.010667314782975073 0.009718707321816513 0.04523996263742447 0.030684769153594967 0.00016428592789452526 0.0003000017604790628 0.00025 0.0002500000000000001 0.0 5.421010862427521e-20 0.00025 0.00025 0.0059988964 0.008273507 0.004459505999999999 0.0035852243 0.02182284 0.013593527 0.0017576158 0.0028905305 0.02046494 0.008859338000000001 0.037309642999999997 0.0034312569
18 17 781.0 533.0 0.0 4198.0 3306.0 4198.0 3306.0 129.0 257.0 4198.0 3306.0 7.0 1.0 1.0 5.0 15.0 55.0 0.0 0.006336112672215678 0.007539691670444881 0.008304921845849803 0.009121751516234046 0.03077610209584236 0.03060857020318508 0.0001919775095302612 0.00022119178902357817 0.00025 0.0 0.00025 0.00025 0.00534597 0.00609803 0.0037583890000000004 0.0035867486 0.014207858 0.013320565 0.0021436373 0.002192039
19 18 841.0 585.0 0.0 4438.0 3511.0 4438.0 3511.0 240.0 205.0 4438.0 3511.0 9.0 7.0 2.0 0.0 15.0 0.0 0.0 0.005212442750053015 0.005838182990213253 0.009321893945503649 0.007284520898622485 0.044901248067617416 0.01573643647134304 0.00017039758677128702 0.00017755883163772523 0.00025 0.0 0.00025 0.00025 0.004599525 0.0050161253 0.004160135 0.0035143814 0.021621725 0.010200171999999999 0.0018743542999999998 0.0018448817 0.026541417 0.010017343 0.039867945 0.010387515
20 19 900.0 632.0 0.0 4674.0 3701.0 4674.0 3701.0 236.0 190.0 4674.0 3701.0 1.0 5.0 4.0 2.0 50.0 25.0 0.0 0.0056087239258192575 0.006030775471020767 0.008758416501990723 0.008624205468393242 0.030815929174423218 0.030819704756140712 0.0001515848562121391 0.00014619529247283936 0.00025 0.0002500000000000001 0.0 5.421010862427521e-20 0.00025 0.00025 0.004823039 0.0049460824 0.0045088935 0.004009163499999999 0.020195715 0.014025801000000001 0.0017171134 0.0014488354999999998 0.013790057 0.008472809 0.030765671 0.00264489
21 20 938.0 680.0 0.0 4824.0 3891.0 4824.0 3891.0 150.0 190.0 4824.0 3891.0 8.0 4.0 3.0 0.0 70.0 0.0 0.0 0.007760794000013878 0.0062177468741235024 0.009675916785758777 0.009185724365931972 0.030620882287621495 0.030945468693971637 0.00015613727737218142 0.00016168373986147344 0.0002500000000000001 0.00025 5.421010862427521e-20 0.0 0.00025 0.00025 0.005812586999999999 0.0048525543 0.005044727 0.0041472296999999995 0.020025931 0.014570421 0.0016528887 0.0015842235999999998 0.019592494 0.010325795 0.031668893999999996 0.0011506106
22 21 998.0 729.0 0.0 5064.0 4090.0 5064.0 4090.0 240.0 199.0 5064.0 4090.0 5.0 9.0 4.0 40.0 50.0 0.0 0.006734883858734974 0.0066686894893717525 0.01012093517361649 0.009633275859106637 0.04593200609087944 0.03070710971951485 0.00015258873463608325 0.00016243076242972163 0.00025 0.0 0.00025 0.00025 0.0050609284 0.005217642 0.004594935 0.0044929385 0.022529753 0.01947369 0.0015191837 0.00170155 0.03485959 0.021158978 0.008621039 0.011703089 0.045938405999999994 0.040702187 0.022119842 0.0043722745
23 22 1018.0 804.0 0.0 5145.0 4390.0 5145.0 4390.0 81.0 300.0 5145.0 4390.0 6.0 9.0 1.0 5.0 15.0 60.0 0.0 0.006149832173832692 0.007745159575574075 0.008713121196366134 0.010264969595287615 0.030442113056778908 0.0457281582057476 0.00017446294077672064 0.0001735425612423569 0.0002500000000000001 5.421010862427521e-20 0.00025 0.00025 0.0048099845 0.005858070999999999 0.0038082603999999996 0.004608556 0.013654656999999999 0.02206757 0.0017780153 0.0016346257 0.020728394 0.007859956 0.031289592000000005 0.005306163000000001
24 23 1043.0 862.0 0.0 5244.0 4619.0 5244.0 4619.0 99.0 229.0 5244.0 4619.0 6.0 0.0 3.0 0.0 30.0 0.0 0.005615412016632035 0.0073957111507249795 0.010374376530714843 0.010238454419935428 0.04561242461204529 0.045198917388916016 0.00014486268628388646 0.00018422666471451518 0.00025 0.0 0.00025 0.00025 0.004630377 0.005702048 0.0049269767 0.0044779684 0.022269842999999998 0.019911936 0.0015429979999999999 0.0017970852
25 24 1096.0 882.0 0.0 5458.0 4699.0 5458.0 4699.0 214.0 80.0 5458.0 4699.0 4.0 6.0 4.0 0.0 50.0 0.0 0.0 0.007605906390587201 0.007019620326900622 0.011603800537879164 0.010097406329511898 0.045488771051168435 0.030556553974747658 0.00015640168567188084 0.00021815481886733326 0.00025 0.0002500000000000001 0.0 5.421010862427521e-20 0.00025 0.00025 0.005479771 0.0058081313 0.005164023 0.0048652836 0.022413598 0.019417763 0.0016174479999999998 0.0023302154 0.016956704 0.0052281856 0.027700908 0.010438837
26 25 1112.0 945.0 0.0 5522.0 4951.0 5522.0 4951.0 64.0 252.0 5522.0 4951.0 4.0 5.0 2.0 4.0 15.0 50.0 0.0 0.007761852173644002 0.005672615208563262 0.010677922322799452 0.007662691003304536 0.03070169128477573 0.02989559806883335 0.00017511004989501092 0.00017223272880073634 0.00025 0.0 0.00025 0.00025 0.005504951 0.004905078 0.004377631 0.0036695688 0.014095218999999999 0.013604815 0.0019523804999999998 0.0018130213 0.031090358 0.004259917 0.037605517000000005 0.025178626000000003
27 26 995.0 0.0 5152.0 5152.0 201.0 5152.0 8.0 2.0 15.0 0.0 0.006299167421530001 0.008046825175071413 0.030643418431282043 0.00017975828086491674 0.00025 0.0 0.00025 0.00025 0.005176335 0.003915025999999999 0.014020402 0.0017273452 0.026928194 0.009660842 0.041887067 0.010047999
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29 28 1072.0 0.0 5462.0 5462.0 220.0 5462.0 3.0 2.0 55.0 0.0 0.008864355713336004 0.011839999879392651 0.046134039759635925 0.0001610755716683343 0.00025 0.0 0.00025 0.00025 0.0062677735 0.0050042396999999995 0.020127073 0.0017192016 0.02061991 0.0075885756 0.034549624 0.010614768
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