Fitted Q-iteration in continuous action-space MDPs

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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András Antos, Csaba Szepesvári, Rémi Munos


We consider continuous state, continuous action batch reinforcement learning where the goal is to learn a good policy from a sufficiently rich trajectory generated by another policy. We study a variant of fitted Q-iteration, where the greedy action selection is replaced by searching for a policy in a restricted set of candidate policies by maximizing the average action values. We provide a rigorous theoretical analysis of this algorithm, proving what we believe is the first finite-time bounds for value-function based algorithms for continuous state- and action-space problems.