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Fitted Q-iteration in continuous action-space MDPs

Andras Antos 1 Rémi Munos 2 Csaba Szepesvari 3
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
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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Contributor : Rémi Munos <>
Submitted on : Monday, November 5, 2007 - 5:34:16 PM
Last modification on : Wednesday, December 11, 2019 - 5:10:02 PM
Document(s) archivé(s) le : Monday, April 12, 2010 - 1:23:42 AM


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  • HAL Id : inria-00185311, version 1


Andras Antos, Rémi Munos, Csaba Szepesvari. Fitted Q-iteration in continuous action-space MDPs. [Technical Report] 2007, pp.22. ⟨inria-00185311v1⟩



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