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,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/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,,,,,,,,,,,,,,,,0.1810549437584988,0.08342612458204374,0.3657155727590055,0.012114535848885052,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
2,0.0,1.0,2002.0,2.0,1001.0,2002.0,0.0,,,1.0,,,,,,,,,,,,,,,,0.10514369548395547,0.05043065738920054,0.21524430347618226,0.0011643643789458708,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
3,1000.0,0.0,3003.0,3.0,1001.0,3003.0,-0.1185302492771778,6.74600433432463,67.46004334324633,1.0,,,,1.0688050798876248e-05,1.766983092613708e-05,0.0002591642551124096,1.702799409031286e-06,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,0.0045520826,0.0030055756,0.023246742999999997,0.00061706273,0.6303898598111053,0.14191577052955434,0.8002050670805612,0.012917920234129009,,,,,,,,,,,,,,,,,,,,,0.00010149915000000001,0.17838655,0.24224899999999996,-0.3742638,0.031711048067017016,0.06663038905079713,0.16946774334467746,-0.1425395913783225,-1.1330065308041135,0.2760152561198482,-0.11754712369609001,-1.564363479104263
4,2001.0,0.0,4004.0,4.0,1001.0,4004.0,-0.2048510260598676,7.668551490997389,76.68551490997385,1.0,,,,3.408961573131819e-05,6.750553695485743e-05,0.0007010267581790687,1.4872452993586194e-06,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,0.010960271,0.013024458000000001,0.09523078,0.0007027931,0.6965929526733899,0.1622994335943064,0.9453308568504694,0.013324665458032537,,,,,,,,,,,,,,,,,,,,,0.0027730353,0.18788128,0.250535,-0.3431186,0.029557790599657345,0.11711363926377175,0.2386419371743873,-0.3346987397531008,-1.113269411960027,0.1980527596764508,-0.6249308459515377,-1.5458447591062914
5,3002.0,0.0,5005.0,5.0,1001.0,5005.0,-0.02134772535498328,7.368122753870011,73.6812275387001,0.0,,,,1.2014473524686764e-05,1.2546101794472948e-05,0.00015411879576276988,1.0827171763594379e-06,0.00010000000000000003,2.7105054312137605e-20,0.0001,0.0001,0.007843023000000001,0.0051095295,0.038092513,0.0006715836400000001,0.66996138932216,0.15703584718374808,0.8908932610369035,0.0060312519674541746,,,,,,,,,,,,,,,,,,,,,0.015175661000000002,0.18127811,0.24180134,-0.31560785,0.03080747100578105,0.13684940389057226,0.2399127439483701,-0.3243346170643305,-0.9330713833476759,0.12577776040520755,-0.5994658138545264,-1.21464647257472