An Analysis of Reinforcement Learning with Function Approximation
published: Aug. 12, 2008, recorded: July 2008, views: 5579
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We address the problem of computing the optimal Q-function in Markov decision problems with infinite state-space. We analyze the convergence properties of several variations of Q-learning when combined with function approximation, extending the analysis of TD-learning in (Tsitsilis and Van Roy, 1996) to stochastic control settings. We identify conditions under which such approximate methods converge with probability 1. We conclude with a brief discussion on the general applicability of our results and compare them with several related works.
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