In [1]:
import pandas as pd
import seaborn as sns
from Agent_adjust import Agent
sns.set_style('whitegrid')
%matplotlib inline
GAME = 'CartPole-v0'
MAX_EPISODES = 1000
class NewAgent(Agent):
def __init__(self, env_name):
super().__init__(env_name)
def run_plot(self, MAX_EPISODES):
while self.episode < MAX_EPISODES:
self.run_adjust(MAX_EPISODES, 50)
self.episode += 1
self.accumulate_reward_list.append(self.accumulate_reward)
print (self.episode, self.accumulate_reward)
print('done')
def main():
A = NewAgent(GAME)
A.run_plot(MAX_EPISODES)
pd.Series(A.accumulate_reward_list).plot(figsize=(16,6))
if __name__ == '__main__':
main()
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18250 3.2233 26.3755 -30.32
18265 1.28718 18.8417 -74.0415
18274 1.04595 15.6038 -49.246
18286 1.25782 36.4516 -70.0192
18300 0.0154072 25.4375 -82.2058
18318 -2.99331 121.59 -104.576
18335 -8.72404 151.852 -91.0583
18371 -5.73455 92.2896 -206.035
18404 -9.44084 450.909 -185.937
18454 1.57443 69.1146 -306.609
18504 -7.46532 53.3389 -297.59
18554 -13.7429 197.64 -299.667
18583 -1.45981 110.74 -125.946
18617 -4.55401 216.328 -205.174
18649 -2.72012 148.084 -202.329
18679 -1.41176 170.845 -184.49
18700 -3.18011 145.546 -138.056
18717 -6.12398 188.662 -109.878
18739 -2.40423 129.096 -142.543
18751 2.91271 272.578 -61.9273
18761 -0.234948 76.212 -53.7983
18770 -0.167947 73.0447 -47.5234
18783 0.107201 100.727 -78.8907
18795 0.831191 54.9616 -66.77
18804 -0.376096 43.6171 -48.882
18821 -2.50573 275.218 -98.8898
18851 1.50315 196.592 -156.485
18862 -6.19991 69.255 -47.0553
18881 -0.272433 107.694 -124.46
18931 2.47343 151.074 -329.575
18953 -3.00539 218.157 -130.347
18991 -13.7829 307.211 -223.981
19023 2.51855 216.449 -197.485
19046 -2.44056 249.573 -137.856
19069 -0.735394 128.669 -151.789
19091 -7.10171 121.669 -135.072
19105 -4.27698 80.6644 -84.998
19131 -2.79114 151.166 -148.671
19166 0.735424 63.3467 -223.578
19180 -5.03191 125.638 -86.198
19206 3.23416 151.863 -166.099
19225 -0.454377 223.697 -119.832
19241 0.710813 148.063 -95.1755
19254 -0.286514 186.042 -79.259
19273 3.54459 153.482 -113.444
19290 -1.59349 137.83 -101.435
19303 -7.01463 145.594 -76.6142
19326 -2.84503 106.665 -145.202
19339 -1.32841 51.3446 -86.5066
19382 0.135716 94.6797 -281.74
19432 3.55402 126.088 -299.088
19435 1.1117 17.1099 -18.9069
19452 1.66895 56.8858 -105.844
19465 0.663454 38.0258 -86.0148
19488 1.19202 42.5246 -155.501
19505 -3.53466 84.5037 -107.946
19555 -6.42161 148.356 -304.583
19557 -7.83009 83.1079 -8.35544
19590 -0.636642 67.7721 -188.414
19625 -3.32763 46.0245 -219.401
19643 -4.50284 230.366 -118.633
19685 -0.471195 167.793 -280.18
19709 0.461379 82.8213 -154.407
19729 -3.89993 87.4184 -132.652
19751 -1.92756 103.474 -150.291
19778 -3.0614 166.131 -177.767
19791 0.851945 154.712 -85.5169
19811 -0.220493 49.927 -129.31
19825 -6.93102 69.5663 -84.681
19859 -1.90327 129.489 -221.266
19871 -1.82558 37.6981 -78.1193
19891 -2.53168 354.576 -129.498
19905 -4.09972 59.1784 -91.471
19920 -1.19873 79.1068 -99.2287
19930 -3.9089 86.6558 -64.9356
19958 5.37195 227.18 -177.739
19984 2.23185 93.0332 -170.277
20003 -2.32607 51.2655 -120.43
20034 2.77017 153.787 -198.381
20051 0.0849049 57.43 -114.39
20071 2.44803 148.25 -123.726
20106 5.70146 75.0188 -213.346
20119 -2.9805 78.475 -81.2906
20169 1.55676 118.536 -289.145
20219 2.44209 19.8894 -328.434
20245 -4.38371 78.2338 -167.895
20274 -3.23123 24.8178 -190.279
20313 -6.34966 169.744 -258.75
20329 -4.83009 56.8916 -98.1352
20341 -0.156117 32.8659 -66.6143
20353 -1.50611 22.149 -65.4229
20373 4.6328 34.9458 -106.388
20389 1.5591 63.5234 -105.948
20403 1.1224 36.4369 -87.0085
20412 -0.521637 15.8168 -58.3827
20450 -2.82128 206.698 -206.314
20468 -0.81383 48.7425 -116.758
20486 -1.26036 68.5159 -120.919
20503 -6.15667 253.746 -86.656
20536 -2.10948 59.3057 -213.879
20586 2.30409 309.862 -306.349
20617 -4.12891 98.8122 -185.814
20665 -10.2516 146.648 -310.746
20695 -0.190022 390.75 -187.617
20731 -5.55889 135.216 -229.457
20781 -6.3813 99.1346 -324.402
20831 -2.35267 189.897 -313.664
20845 -4.24913 74.707 -86.0857
20865 -2.93401 156.451 -135.328
20874 -0.149903 91.4871 -31.548
20886 2.68298 64.0133 -39.9196
20897 0.00263608 214.66 -68.4694
20909 -3.3576 51.7227 -69.1747
20917 -0.325379 13.5464 -29.6059
20927 0.697846 16.8502 -50.9222
20937 1.60169 36.0249 -57.4466
20947 0.98105 81.1982 -60.0565
20959 3.52434 74.0238 -69.3423
20970 -3.91971 78.6373 -67.9643
20986 -0.925015 79.6269 -101.52
21009 -1.44677 118.062 -148.072
21029 -6.55417 305.229 -113.041
21078 -1.62105 303.647 -283.78
21105 -4.83436 199.102 -165.708
21135 -9.69658 195.14 -166.603
21158 -14.3983 176.93 -123.092
21184 -7.40633 254.502 -152.355
21234 -8.7814 221.935 -299.181
21276 10.5505 2453.3 -213.829
21326 -3.98498 17.662 -289.249
21376 -12.7556 691.026 -222.593
21402 -13.8927 621.785 -129.378
21427 6.11626 204.901 -161.408
21451 2.40871 511.601 -149.532
21461 -1.07507 183.858 -64.8585
21481 4.33143 172.73 -132.412
21495 0.429591 113.216 -93.0364
21505 -2.65763 65.1502 -64.1812
21536 -1.0662 340.738 -179.65
21568 -0.232568 745.585 -192.127
21582 -0.238442 180.462 -73.1307
21594 0.857876 160.744 -59.3825
21604 -5.80096 127.323 -50.7321
done
1001 223.3944905849538
done
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Content source: AlphaSmartDog/DeepLearningNotes
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