# Finite-Sample Analysis of Least-Squares Policy Iteration

1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
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 $\beta$-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.
Keywords :
Document type :
Journal articles

Cited literature [30 references]

https://hal.inria.fr/hal-00772060
Contributor : Alessandro Lazaric <>
Submitted on : Wednesday, January 9, 2013 - 6:52:59 PM
Last modification on : Thursday, June 27, 2019 - 1:36:06 PM
Long-term archiving on : Wednesday, April 10, 2013 - 3:56:32 AM

### File

lazaric12a.pdf
Files produced by the author(s)

### Identifiers

• HAL Id : hal-00772060, version 1

### Citation

Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos. Finite-Sample Analysis of Least-Squares Policy Iteration. Journal of Machine Learning Research, Microtome Publishing, 2012, 13, pp.3041-3074. ⟨hal-00772060⟩

Record views