Finite-Sample Analysis of LSTD
Alessandro Lazaric, Mohammad Ghavamzadeh and Rémi Munos
In: Twenty-Seventh International Conference on Machine Learning (ICML-2010), 21-24 June 2010, Haifa, Israel.

## Abstract

In this paper we consider the problem of policy evaluation in reinforcement learning, i.e., learning the value function of a fixed policy, using the least-squares temporal-difference (LSTD) learning algorithm. We report a finite-sample analysis of LSTD. 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 $\beta$-mixing.

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EPrint Type: Conference or Workshop Item (Paper) Project Keyword UNSPECIFIED Learning/Statistics & Optimisation 7228 Alessandro Lazaric 12 March 2011