Asynchronous Methods for Deep Reinforcement Learning
The process continues until the agent reaches a terminal state after which the process restarts. The return R t = P 1 k=0 kr t+k is the total accumulated return from time step twith discount factor 2(0;1]. The goal of the agent is to maximize the expected return from each state s t. The action value Qˇ(s;a) = E[R tjs t= s;a] is the ex-
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