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Code Issues Projects Releases Wiki Activity

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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541
rl_coach/traces/Mujoco_A3C_inverted_pendulum/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
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532,353.0,0.0,21.0,1.0,21.0,5865.0,0.1,1.0500000000000005,21.0,0.0,,,,,,,,,,,,0.26408702,0.0,0.26408702,0.26408702,,,,,0.05533826649188996,0.09263889664527176,0.19149497151374814,-0.08731526136398315,0.09267668,0.027464991,0.13731526,0.05355478,0.0058221440000000005,0.0,0.0058221440000000005,0.0058221440000000005,0.10185404,0.0,0.10185404,0.10185404
533,354.0,0.0,19.0,1.0,19.0,5884.0,0.1,0.9500000000000004,19.0,0.0,,,,,,,,,,,,0.39731845,0.0,0.39731845,0.39731845,,,,,0.3961019739508629,0.2983916021632532,0.8947322368621826,-0.1368432343006134,0.11891205,0.045684277999999995,0.18684323,0.036759794,0.122967176,0.0,0.122967176,0.122967176,0.30748042,0.0,0.30748042,0.30748042
534,355.0,0.0,20.0,1.0,20.0,5904.0,0.1,1.0000000000000002,20.0,0.0,,,,,,,,,,,,0.3166317,0.0,0.3166317,0.3166317,,,,,0.34988576722772496,0.2817061420201337,0.8187878131866455,-0.124776691198349,0.12134943,0.03688914,0.17477669,0.05036908,0.1008892,0.0,0.1008892,0.1008892,0.33015883,0.0,0.33015883,0.33015883
535,356.0,0.0,6.0,1.0,6.0,5910.0,0.1,0.3,6.0,0.0,,,,,,,,,,,,0.4568929,0.0,0.4568929,0.4568929,,,,,0.3740368545055389,0.2874107429401349,0.8465098738670349,-0.1761256605386734,0.119159855,0.045015063,0.22612566,0.05163344,0.11125424,0.0,0.11125424,0.11125424,0.28807685,0.0,0.28807685,0.28807685
536,357.0,0.0,19.0,1.0,19.0,5929.0,0.1,0.9500000000000004,19.0,0.0,,,,,,,,,,,,0.20338742,0.0,0.20338742,0.20338742,,,,,0.07323690255482991,0.1128888306695804,0.2316648811101913,-0.08557571470737457,0.098875426,0.03266615,0.1374505,0.05683118,0.009053766,0.0,0.009053766,0.009053766,0.07099757,0.0,0.07099757,0.07099757
537,358.0,0.0,27.0,1.0,27.0,5956.0,0.1,1.3500000000000003,27.0,0.0,,,,,,,,,,,,0.39596182,0.0,0.39596182,0.39596182,,,,,0.3415213697835019,0.2848132365243439,0.8143702745437622,-0.13791485130786896,0.12971382,0.03941476,0.18791485,0.05478664,0.09887773,0.0,0.09887773,0.09887773,0.3038504,0.0,0.3038504,0.3038504
538,359.0,0.0,29.0,1.0,29.0,5985.0,0.1,1.4500000000000006,29.0,0.0,,,,,,,,,,,,0.50080687,0.0,0.50080687,0.50080687,,,,,0.4670854343308343,0.4321811750842292,1.1216620206832886,-0.33334973454475403,0.17586432,0.09432597,0.38334975,0.06264118,0.20247468,0.0,0.20247468,0.20247468,0.32563025,0.0,0.32563025,0.32563025
539,360.0,0.0,5.0,1.0,5.0,5990.0,0.1,0.25,5.0,0.0,,,,,,,,,,,,0.5332048,0.0,0.5332048,0.5332048,,,,,0.527356720946986,0.37651555366758777,1.2038553953170776,-0.17069163918495178,0.15713248,0.051937263,0.26247704,0.060284104000000005,0.20993453,0.0,0.20993453,0.20993453,0.5655926,0.0,0.5655926,0.5655926
540,361.0,0.0,6.0,1.0,6.0,5996.0,0.1,0.3,6.0,0.0,,,,,,,,,,,,0.24390347,0.0,0.24390347,0.24390347,,,,,0.05144660919904709,0.09215068514165076,0.1907046884298325,-0.08534099161624907,0.09656833,0.0259755,0.13534099,0.054345055999999996,0.0055692517000000006,0.0,0.0055692517000000006,0.0055692517000000006,0.048557755,0.0,0.048557755,0.048557755
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
2 1 0.0 1.0 5.0 1.0 5.0 5.0 0.1 0.0
3 2 0.0 1.0 6.0 1.0 6.0 11.0 0.1 0.0
4 3 0.0 1.0 4.0 1.0 4.0 15.0 0.1 0.0
5 4 0.0 1.0 14.0 1.0 14.0 29.0 0.1 0.0
6 5 0.0 1.0 5.0 1.0 5.0 34.0 0.1 0.0
7 6 0.0 1.0 7.0 1.0 7.0 41.0 0.1 0.0
8 7 0.0 1.0 11.0 1.0 11.0 52.0 0.1 0.0
9 8 0.0 1.0 4.0 1.0 4.0 56.0 0.1 0.0
10 9 0.0 1.0 10.0 1.0 10.0 66.0 0.1 0.0
11 10 0.0 1.0 6.0 1.0 6.0 72.0 0.1 0.0
12 11 0.0 1.0 9.0 1.0 9.0 81.0 0.1 0.0
13 12 0.0 1.0 3.0 1.0 3.0 84.0 0.1 0.0
14 13 0.0 1.0 10.0 1.0 10.0 94.0 0.1 0.0
15 14 0.0 1.0 3.0 1.0 3.0 97.0 0.1 0.0
16 15 0.0 1.0 3.0 1.0 3.0 100.0 0.1 0.0
17 16 0.0 1.0 7.0 1.0 7.0 107.0 0.1 0.0
18 17 0.0 1.0 7.0 1.0 7.0 114.0 0.1 0.0
19 18 0.0 1.0 5.0 1.0 5.0 119.0 0.1 0.0
20 19 0.0 1.0 4.0 1.0 4.0 123.0 0.1 0.0
21 20 0.0 1.0 12.0 1.0 12.0 135.0 0.1 0.0
22 21 0.0 1.0 7.0 1.0 7.0 142.0 0.1 0.0
23 22 0.0 1.0 5.0 1.0 5.0 147.0 0.1 0.0
24 23 0.0 1.0 3.0 1.0 3.0 150.0 0.1 0.0
25 24 0.0 1.0 6.0 1.0 6.0 156.0 0.1 0.0
26 25 0.0 1.0 7.0 1.0 7.0 163.0 0.1 0.0
27 26 0.0 1.0 5.0 1.0 5.0 168.0 0.1 0.0
28 27 0.0 1.0 9.0 1.0 9.0 177.0 0.1 0.0
29 28 0.0 1.0 5.0 1.0 5.0 182.0 0.1 0.0
30 29 0.0 1.0 5.0 1.0 5.0 187.0 0.1 0.0
31 30 0.0 1.0 4.0 1.0 4.0 191.0 0.1 0.0
32 31 0.0 1.0 8.0 1.0 8.0 199.0 0.1 0.0
33 32 0.0 1.0 8.0 1.0 8.0 207.0 0.1 0.0
34 33 0.0 1.0 18.0 1.0 18.0 225.0 0.1 0.0
35 34 0.0 1.0 13.0 1.0 13.0 238.0 0.1 0.0
36 35 0.0 1.0 13.0 1.0 13.0 251.0 0.1 0.0
37 36 0.0 1.0 9.0 1.0 9.0 260.0 0.1 0.0
38 37 0.0 1.0 3.0 1.0 3.0 263.0 0.1 0.0
39 38 0.0 1.0 5.0 1.0 5.0 268.0 0.1 0.0
40 39 0.0 1.0 4.0 1.0 4.0 272.0 0.1 0.0
41 40 0.0 1.0 3.0 1.0 3.0 275.0 0.1 0.0
42 41 0.0 1.0 5.0 1.0 5.0 280.0 0.1 0.0
43 42 0.0 1.0 3.0 1.0 3.0 283.0 0.1 0.0
44 43 0.0 1.0 8.0 1.0 8.0 291.0 0.1 0.0
45 44 0.0 1.0 5.0 1.0 5.0 296.0 0.1 0.0
46 45 0.0 1.0 4.0 1.0 4.0 300.0 0.1 0.0
47 46 0.0 1.0 5.0 1.0 5.0 305.0 0.1 0.0
48 47 0.0 1.0 4.0 1.0 4.0 309.0 0.1 0.0
49 48 0.0 1.0 3.0 1.0 3.0 312.0 0.1 0.0
50 49 0.0 1.0 12.0 1.0 12.0 324.0 0.1 0.0
51 50 0.0 1.0 7.0 1.0 7.0 331.0 0.1 0.0
52 51 0.0 1.0 4.0 1.0 4.0 335.0 0.1 0.0
53 52 0.0 1.0 4.0 1.0 4.0 339.0 0.1 0.0
54 53 0.0 1.0 4.0 1.0 4.0 343.0 0.1 0.0
55 54 0.0 1.0 3.0 1.0 3.0 346.0 0.1 0.0
56 55 0.0 1.0 3.0 1.0 3.0 349.0 0.1 0.0
57 56 0.0 1.0 4.0 1.0 4.0 353.0 0.1 0.0
58 57 0.0 1.0 4.0 1.0 4.0 357.0 0.1 0.0
59 58 0.0 1.0 10.0 1.0 10.0 367.0 0.1 0.0
60 59 0.0 1.0 6.0 1.0 6.0 373.0 0.1 0.0
61 60 0.0 1.0 5.0 1.0 5.0 378.0 0.1 0.0
62 61 0.0 1.0 8.0 1.0 8.0 386.0 0.1 0.0
63 62 0.0 1.0 6.0 1.0 6.0 392.0 0.1 0.0
64 63 0.0 1.0 3.0 1.0 3.0 395.0 0.1 0.0
65 64 0.0 1.0 11.0 1.0 11.0 406.0 0.1 0.0
66 65 0.0 1.0 4.0 1.0 4.0 410.0 0.1 0.0
67 66 0.0 1.0 3.0 1.0 3.0 413.0 0.1 0.0
68 67 0.0 1.0 3.0 1.0 3.0 416.0 0.1 0.0
69 68 0.0 1.0 3.0 1.0 3.0 419.0 0.1 0.0
70 69 0.0 1.0 4.0 1.0 4.0 423.0 0.1 0.0
71 70 0.0 1.0 5.0 1.0 5.0 428.0 0.1 0.0
72 71 0.0 1.0 4.0 1.0 4.0 432.0 0.1 0.0
73 72 0.0 1.0 3.0 1.0 3.0 435.0 0.1 0.0
74 73 0.0 1.0 11.0 1.0 11.0 446.0 0.1 0.0
75 74 0.0 1.0 9.0 1.0 9.0 455.0 0.1 0.0
76 75 0.0 1.0 3.0 1.0 3.0 458.0 0.1 0.0
77 76 0.0 1.0 3.0 1.0 3.0 461.0 0.1 0.0
78 77 0.0 1.0 9.0 1.0 9.0 470.0 0.1 0.0
79 78 0.0 1.0 3.0 1.0 3.0 473.0 0.1 0.0
80 79 0.0 1.0 6.0 1.0 6.0 479.0 0.1 0.0
81 80 0.0 1.0 5.0 1.0 5.0 484.0 0.1 0.0
82 81 0.0 1.0 9.0 1.0 9.0 493.0 0.1 0.0
83 82 0.0 1.0 4.0 1.0 4.0 497.0 0.1 0.0
84 83 0.0 1.0 3.0 1.0 3.0 500.0 0.1 0.0
85 84 0.0 1.0 3.0 1.0 3.0 503.0 0.1 0.0
86 85 0.0 1.0 3.0 1.0 3.0 506.0 0.1 0.0
87 86 0.0 1.0 4.0 1.0 4.0 510.0 0.1 0.0
88 87 0.0 1.0 8.0 1.0 8.0 518.0 0.1 0.0
89 88 0.0 1.0 3.0 1.0 3.0 521.0 0.1 0.0
90 89 0.0 1.0 3.0 1.0 3.0 524.0 0.1 0.0
91 90 0.0 1.0 4.0 1.0 4.0 528.0 0.1 0.0
92 91 0.0 1.0 3.0 1.0 3.0 531.0 0.1 0.0
93 92 0.0 1.0 6.0 1.0 6.0 537.0 0.1 0.0
94 93 0.0 1.0 3.0 1.0 3.0 540.0 0.1 0.0
95 94 0.0 1.0 5.0 1.0 5.0 545.0 0.1 0.0
96 95 0.0 1.0 8.0 1.0 8.0 553.0 0.1 0.0
97 96 0.0 1.0 6.0 1.0 6.0 559.0 0.1 0.0
98 97 0.0 1.0 7.0 1.0 7.0 566.0 0.1 0.0
99 98 0.0 1.0 7.0 1.0 7.0 573.0 0.1 0.0
100 99 0.0 1.0 4.0 1.0 4.0 577.0 0.1 0.0
101 100 0.0 1.0 4.0 1.0 4.0 581.0 0.1 0.0
102 101 0.0 1.0 3.0 1.0 3.0 584.0 0.1 0.0
103 102 0.0 1.0 5.0 1.0 5.0 589.0 0.1 0.0
104 103 0.0 1.0 6.0 1.0 6.0 595.0 0.1 0.0
105 104 0.0 1.0 4.0 1.0 4.0 599.0 0.1 0.0
106 105 0.0 1.0 4.0 1.0 4.0 603.0 0.1 0.0
107 106 0.0 1.0 4.0 1.0 4.0 607.0 0.1 0.0
108 107 0.0 1.0 7.0 1.0 7.0 614.0 0.1 0.0
109 108 0.0 1.0 9.0 1.0 9.0 623.0 0.1 0.0
110 109 0.0 1.0 3.0 1.0 3.0 626.0 0.1 0.0
111 110 0.0 1.0 6.0 1.0 6.0 632.0 0.1 0.0
112 111 0.0 1.0 5.0 1.0 5.0 637.0 0.1 0.0
113 112 0.0 1.0 4.0 1.0 4.0 641.0 0.1 0.0
114 113 0.0 1.0 7.0 1.0 7.0 648.0 0.1 0.0
115 114 0.0 1.0 3.0 1.0 3.0 651.0 0.1 0.0
116 115 0.0 1.0 14.0 1.0 14.0 665.0 0.1 0.0
117 116 0.0 1.0 8.0 1.0 8.0 673.0 0.1 0.0
118 117 0.0 1.0 4.0 1.0 4.0 677.0 0.1 0.0
119 118 0.0 1.0 5.0 1.0 5.0 682.0 0.1 0.0
120 119 0.0 1.0 10.0 1.0 10.0 692.0 0.1 0.0
121 120 0.0 1.0 9.0 1.0 9.0 701.0 0.1 0.0
122 121 0.0 1.0 5.0 1.0 5.0 706.0 0.1 0.0
123 122 0.0 1.0 3.0 1.0 3.0 709.0 0.1 0.0
124 123 0.0 1.0 18.0 1.0 18.0 727.0 0.1 0.0
125 124 0.0 1.0 9.0 1.0 9.0 736.0 0.1 0.0
126 125 0.0 1.0 3.0 1.0 3.0 739.0 0.1 0.0
127 126 0.0 1.0 7.0 1.0 7.0 746.0 0.1 0.0
128 127 0.0 1.0 8.0 1.0 8.0 754.0 0.1 0.0
129 128 0.0 1.0 10.0 1.0 10.0 764.0 0.1 0.0
130 129 0.0 1.0 6.0 1.0 6.0 770.0 0.1 0.0
131 130 0.0 1.0 4.0 1.0 4.0 774.0 0.1 0.0
132 131 0.0 1.0 4.0 1.0 4.0 778.0 0.1 0.0
133 132 0.0 1.0 6.0 1.0 6.0 784.0 0.1 0.0
134 133 0.0 1.0 6.0 1.0 6.0 790.0 0.1 0.0
135 134 0.0 1.0 3.0 1.0 3.0 793.0 0.1 0.0
136 135 0.0 1.0 4.0 1.0 4.0 797.0 0.1 0.0
137 136 0.0 1.0 5.0 1.0 5.0 802.0 0.1 0.0
138 137 0.0 1.0 5.0 1.0 5.0 807.0 0.1 0.0
139 138 0.0 1.0 5.0 1.0 5.0 812.0 0.1 0.0
140 139 0.0 1.0 6.0 1.0 6.0 818.0 0.1 0.0
141 140 0.0 1.0 11.0 1.0 11.0 829.0 0.1 0.0
142 141 0.0 1.0 4.0 1.0 4.0 833.0 0.1 0.0
143 142 0.0 1.0 3.0 1.0 3.0 836.0 0.1 0.0
144 143 0.0 1.0 7.0 1.0 7.0 843.0 0.1 0.0
145 144 0.0 1.0 5.0 1.0 5.0 848.0 0.1 0.0
146 145 0.0 1.0 7.0 1.0 7.0 855.0 0.1 0.0
147 146 0.0 1.0 3.0 1.0 3.0 858.0 0.1 0.0
148 147 0.0 1.0 4.0 1.0 4.0 862.0 0.1 0.0
149 148 0.0 1.0 15.0 1.0 15.0 877.0 0.1 0.0
150 149 0.0 1.0 4.0 1.0 4.0 881.0 0.1 0.0
151 150 0.0 1.0 5.0 1.0 5.0 886.0 0.1 0.0
152 151 0.0 1.0 5.0 1.0 5.0 891.0 0.1 0.0
153 152 0.0 1.0 4.0 1.0 4.0 895.0 0.1 0.0
154 153 0.0 1.0 6.0 1.0 6.0 901.0 0.1 0.0
155 154 0.0 1.0 6.0 1.0 6.0 907.0 0.1 0.0
156 155 0.0 1.0 3.0 1.0 3.0 910.0 0.1 0.0
157 156 0.0 1.0 6.0 1.0 6.0 916.0 0.1 0.0
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179 178 0.0 1.0 5.0 1.0 5.0 1022.0 0.1 0.0
180 179 0.0 1.0 3.0 1.0 3.0 1025.0 0.1 0.0
181 180 1.0 0.0 8.0 1.0 8.0 1033.0 0.1 0.4 8.0 0.0 1.0175393 0.0 1.0175393 1.0175393 0.12447824080785115 0.017750036188499713 0.14949940145015714 0.11022071540355682 -0.025143243 0.025383925 -0.0009944016 -0.06022072 0.007904948 0.0 0.007904948 0.007904948 0.37719628 0.0 0.37719628 0.37719628
182 181 2.0 0.0 5.0 1.0 5.0 1038.0 0.1 0.25 5.0 0.0 0.36188245 0.0 0.36188245 0.36188245 0.24260595347732306 0.0983288628882763 0.3875662684440613 0.10172899067401886 -0.022777993 0.019388942 0.0005394753 -0.051728994 0.034263108 0.0 0.034263108 0.034263108 0.35309696 0.0 0.35309696 0.35309696
183 182 3.0 0.0 5.0 1.0 5.0 1043.0 0.1 0.25 5.0 0.0 0.59314895 0.0 0.59314895 0.59314895 0.16281317323446273 0.06346651625392281 0.24495960772037506 0.06752246618270874 -0.014798236000000001 0.008952915 9.01336e-05 -0.02708568 0.015268063 0.0 0.015268063 0.015268063 0.17759389 0.0 0.17759389 0.17759389
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