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Reports (Technical Report) Year : 2007

Fitted Q-iteration in continuous action-space MDPs

Rémi Munos
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Csaba Szepesvari
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Abstract

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 some 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 analysis of this algorithm, proving what we believe is the first finite-time bound for value-function based algorithms for continuous state and action problems.
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Dates and versions

inria-00185311 , version 1 (05-11-2007)
inria-00185311 , version 2 (08-01-2008)

Identifiers

  • HAL Id : inria-00185311 , version 2

Cite

Andras Antos, Rémi Munos, Csaba Szepesvari. Fitted Q-iteration in continuous action-space MDPs. [Technical Report] 2007, pp.24. ⟨inria-00185311v2⟩
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