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Trace tests update

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Shadi Endrawis
2018-08-20 13:01:17 +03:00
parent c1f428666e
commit 3abb6cd415
99 changed files with 12876 additions and 39 deletions
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rl_coach/traces/ControlSuite_DDPG_cartpole_swingup/trace.csv Normal file
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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
1,0.0,1.0,1001.0,1.0,1001.0,1001.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
2,0.0,1.0,2002.0,2.0,1001.0,2002.0,0.0,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
3,1000.0,0.0,3003.0,3.0,1001.0,3003.0,-0.1185302492771778,8.62704551591294,86.2704551591296,1.0,,,,1.0509011072599606e-05,4.393642656353033e-05,0.0008535402594134213,1.1514939615153708e-06,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,0.004000389,0.00447183,0.062234186,0.00047969296999999996,,,,,,,,,,,,,,,,,,,,,0.08464705,0.16014087,0.45386302,-0.26037258,0.01247160570665026,0.02153857694844653,0.08672064238048882,-0.04962609781241383,0.3359349988514577,0.6368093944604776,1.3638484370927098,-1.3839266445045957
4,2001.0,0.0,4004.0,4.0,1001.0,4004.0,-0.2048510260598676,17.580070175231974,175.80070175231998,1.0,,,,0.0005509343815205071,0.0018491137578482792,0.023759014904499054,5.607626462733606e-06,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,0.045537997000000004,0.09140324,1.2210321000000002,0.0010273910000000001,,,,,,,,,,,,,,,,,,,,,0.1922657,0.16243528,0.44480476,-0.2532415,0.03993582413609073,0.11728732960908478,0.5736919507147175,-0.26410636501093465,0.6924021347523865,0.5892731229023225,1.3749280698542792,-1.507436630113174
5,3002.0,0.0,5005.0,5.0,1001.0,5005.0,-0.02134772535498328,13.124325999088368,131.24325999088364,0.0,,,,0.0001703916229396802,0.000568676102611858,0.004801726434379816,2.6488642106414773e-06,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,0.014244637,0.014174069,0.10748595,0.00069606147,,,,,,,,,,,,,,,,,,,,,0.38734838,0.23498419,0.6344281,-0.10678842,0.09845966999879296,0.17017726714756395,0.6471482681083021,-0.23208531499469515,0.8583268163158988,0.5493396564055796,1.4005169604031336,-1.084873489999208
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
2 1 0.0 1.0 1001.0 1.0 1001.0 1001.0 0.0 0.0
3 2 0.0 1.0 2002.0 2.0 1001.0 2002.0 0.0 1.0
4 3 1000.0 0.0 3003.0 3.0 1001.0 3003.0 -0.1185302492771778 8.62704551591294 86.2704551591296 1.0 1.0509011072599606e-05 4.393642656353033e-05 0.0008535402594134213 1.1514939615153708e-06 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 0.004000389 0.00447183 0.062234186 0.00047969296999999996 0.08464705 0.16014087 0.45386302 -0.26037258 0.01247160570665026 0.02153857694844653 0.08672064238048882 -0.04962609781241383 0.3359349988514577 0.6368093944604776 1.3638484370927098 -1.3839266445045957
5 4 2001.0 0.0 4004.0 4.0 1001.0 4004.0 -0.2048510260598676 17.580070175231974 175.80070175231998 1.0 0.0005509343815205071 0.0018491137578482792 0.023759014904499054 5.607626462733606e-06 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 0.045537997000000004 0.09140324 1.2210321000000002 0.0010273910000000001 0.1922657 0.16243528 0.44480476 -0.2532415 0.03993582413609073 0.11728732960908478 0.5736919507147175 -0.26410636501093465 0.6924021347523865 0.5892731229023225 1.3749280698542792 -1.507436630113174
6 5 3002.0 0.0 5005.0 5.0 1001.0 5005.0 -0.02134772535498328 13.124325999088368 131.24325999088364 0.0 0.0001703916229396802 0.000568676102611858 0.004801726434379816 2.6488642106414773e-06 0.00010000000000000003 2.7105054312137605e-20 0.0001 0.0001 0.014244637 0.014174069 0.10748595 0.00069606147 0.38734838 0.23498419 0.6344281 -0.10678842 0.09845966999879296 0.17017726714756395 0.6471482681083021 -0.23208531499469515 0.8583268163158988 0.5493396564055796 1.4005169604031336 -1.084873489999208