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In this paper, we analyze a class of actor-critic algorithms under partially observable Markov decision process (POMDP) environment. Specifically, in this work we focus on the two-time-scale framework in which the critic uses a temporal difference with neural network (NN) as nonlinear function approximator, and the actor is updated using greedy algorithm with the stochastic gradient approach. Instead...
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