PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Rollout Sampling Approximate Policy Iteration
Christos Dimitrakakis and Michail G. Lagoudakis
Machine Learning Volume 72, Number 3, pp. 157-171, 2008.

Abstract

Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4181
Deposited By:Christos Dimitrakakis
Deposited On:21 October 2008