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,Q/Mean,Q/Stdev,Q/Max,Q/Min,Q Values/Mean,Q Values/Stdev,Q Values/Max,Q Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min
1,0.0,1.0,486.0,1.0,486.0,486.0,0.5,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
2,0.0,1.0,87.0,1.0,87.0,573.0,0.5,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
3,0.0,1.0,149.0,1.0,149.0,722.0,0.5,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
4,0.0,1.0,335.0,1.0,335.0,1057.0,0.5,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
5,36.0,0.0,180.0,1.0,180.0,1237.0,0.4982359999999992,3.0,30.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.0709828,0.02884768,0.14847249,-0.025073245,0.15536511,0.67742556,4.0763674,0.00025629386
6,51.0,0.0,74.0,1.0,74.0,1311.0,0.4975107999999989,2.0,15.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.09154996,0.034854878,0.17196007,0.029762035,0.06300321,0.1426023,0.49494484,0.0034788419
7,77.0,0.0,131.0,1.0,131.0,1442.0,0.4962269999999984,2.0,35.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.07617202,0.023897866,0.14918622,0.035134307999999996,0.016550515,0.04922439,0.18912512,0.00011201962
8,118.0,0.0,204.0,1.0,204.0,1646.0,0.4942277999999975,2.0,15.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.059103607999999995,0.020852849,0.11587171,0.006847885400000001,0.03776522,0.16741602,0.9484094999999999,7.4577442e-06
9,137.0,0.0,92.0,1.0,92.0,1738.0,0.4933261999999971,1.0,5.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.07352363,0.034035592999999996,0.15143472,0.0044105817,0.05140639,0.20671843,0.9284075999999999,0.00020915868000000003
10,201.0,0.0,321.0,1.0,321.0,2059.0,0.4901803999999957,10.0,115.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.08682183,0.04259716,0.30125758,0.0020528187,0.08377568,0.18461238,0.6967797,0.00044282363
11,265.0,0.0,317.0,1.0,317.0,2376.0,0.4870737999999944,8.0,130.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.107165605,0.036707986,0.23180877,0.01380832,0.09232898,0.22525571,0.87342286,0.0018040526999999998
12,310.0,0.0,224.0,1.0,224.0,2600.0,0.4848785999999934,3.0,20.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.086089715,0.027629882,0.19709364,0.025783142000000002,0.025214866000000002,0.08048548,0.39741653,9.443184499999999e-05
13,338.0,0.0,138.0,1.0,138.0,2738.0,0.4835261999999929,1.0,10.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.056624517,0.014742057,0.09184578,0.023095844,0.013557022,0.06671045,0.36011392,7.6326745e-05
14,378.0,0.0,200.0,1.0,200.0,2938.0,0.481566199999992,1.0,5.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.069043785,0.030751564,0.16467288,0.0038135927,0.01971127,0.1051151,0.6758529999999999,4.7275649999999995e-05
15,422.0,0.0,221.0,1.0,221.0,3159.0,0.4794003999999911,3.0,30.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.06917530000000001,0.027813602000000003,0.13201289,0.016976256000000002,0.04862738,0.18498637,1.0439113000000002,6.747579e-05
16,460.0,0.0,190.0,1.0,190.0,3349.0,0.4775383999999903,2.0,45.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.056344293,0.023635779,0.10303167,-0.009649781,0.032846852999999995,0.14394106,0.83443564,0.00015374989
17,511.0,0.0,255.0,1.0,255.0,3604.0,0.4750393999999892,6.0,75.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.07982238,0.03705571,0.15894309,-0.003064227,0.07116045,0.20118825,0.8639594999999999,0.00021242326000000001
18,583.0,0.0,360.0,1.0,360.0,3964.0,0.4715113999999877,4.0,50.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.0702058,0.03336375,0.18290229,-0.0028483917,0.027493622000000002,0.12953442,1.0179706,4.404120299999999e-06
19,623.0,0.0,199.0,1.0,199.0,4163.0,0.4695611999999868,2.0,35.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.06575828,0.020313479,0.1296886,0.031751662,0.02896362,0.14715028,0.930523,9.032046999999998e-05
20,673.0,0.0,247.0,1.0,247.0,4410.0,0.4671405999999858,5.0,35.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.08191794,0.025515838,0.16764577,0.02347238,0.051037148,0.15077179999999998,0.6975503000000001,2.4204688e-05