PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

LSTD with Random Projections
Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard and Rémi Munos
Advances in Neural Information Processing Systems Volume 23, pp. 721-729, 2010.

Abstract

We consider the problem of reinforcement learning in high-dimensional spaces when the number of features is bigger than the number of samples. In particular, we study the least-squares temporal difference (LSTD) learning algorithm when a space of low dimension is generated with a random projection from a highdimensional space. We provide a thorough theoretical analysis of the LSTD with random projections and derive performance bounds for the resulting algorithm. We also show how the error of LSTD with random projections is propagated through the iterations of a policy iteration algorithm and provide a performance bound for the resulting least-squares policy iteration (LSPI) algorithm.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7417
Deposited By:Odalric-Ambrym Maillard
Deposited On:17 March 2011