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timestamp_reward.py
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import matplotlib.pyplot as plt
import seaborn as sb
import numpy as np
lr_reward = [-0.18939808651123391, -0.051395551832964612, -0.095584828807171451, -0.13320952797660821, -0.060392471318332405, -0.11833840145384193, -0.34023053181778867, -0.078557690937867394, -0.14794543793853798, -0.085842780352227505, -0.054999296808213007, -0.1628112045431688, 0.057479946328866556, -0.038393297949069524, -0.19240457408040484, -0.12396271020529659, -0.099968972869540706, -0.28412231929474163, -0.07649643697482103, -0.092356692115800984, -0.1696809958559943, 0.057044200355607186, -0.1916164505060933, -0.12357499391806148, -0.16434516144011285, -0.052359215622216765, -0.34788444916593303, -0.13880530871726082, -0.13487116647843012, -0.17565935634322075, -0.012983967137714882, -0.08104695062411188, -0.31563799058104025, -0.26193109712888757, 0.027036113675910373, 0.046948675088294554, -0.036996657397094117, -0.19304840588338684, -0.079992227084790452, -0.050079143848832261, -0.039242419586101598, -0.21243232943221663, -0.35082062656909352, -0.12897948377369345, 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# plt.plot(range(906, 1813), lr_reward, label='lr')
plt.plot(range(906, 1813), np.array(lr_reward).cumsum(), label='etr')
plt.legend(loc='best')
plt.show()
print(np.mean(lr_reward))