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''' Script for Cartpole using policy gradient via Chainer, two layer MLP, dropout, and rejection sampling of historical memories ''' | |
import gym | |
import numpy as np | |
import chainer | |
from chainer import optimizers | |
from chainer import ChainList, Variable | |
import chainer.functions as F |