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Finite-sample analysis of least-squares policy iteration

Authors:

Alessandro Lazaric,

Mohammad Ghavamzadeh,

Rémi MunosAuthors Info & Claims

The Journal of Machine Learning Research, Volume 13, Issue 1

Pages 3041 - 3074

Published: 01 October 2012 Publication History

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Abstract

In this paper, we report a performance bound for the widely used least-squares policy iteration (LSPI) algorithm. We first consider the problem of policy evaluation in reinforcement learning, that is, learning the value function of a fixed policy, using the least-squares temporal-difference (LSTD) learning method, and report finite-sample analysis for this algorithm. To do so, we first derive a bound on the performance of the LSTD solution evaluated at the states generated by the Markov chain and used by the algorithm to learn an estimate of the value function. This result is general in the sense that no assumption is made on the existence of a stationary distribution for the Markov chain. We then derive generalization bounds in the case when the Markov chain possesses a stationary distribution and is b-mixing. Finally, we analyze how the error at each policy evaluation step is propagated through the iterations of a policy iteration method, and derive a performance bound for the LSPI algorithm.

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Finite-sample analysis of least-squares policy iteration

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 13, Issue 1

January 2012

3712 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

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Published: 01 October 2012

Published in JMLR Volume 13, Issue 1

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Cited By

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