PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Algorithms and Bounds for Rollout Sampling Approximate Policy Iteration
Christos Dimitrakakis and Michail G. Lagoudakis
In: European Workshop on Reinforcement Learning EWRL 2008, 30 Jun - 04 Jul 2008, Lille, France.

Abstract

Abstract: Several approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem, have been proposed recently. Finding good policies with such methods requires not only an appropriate classifier, but also reliable examples of best actions, covering the state space sufficiently. Up to this time, little work has been done on appropriate covering schemes and on methods for reducing the sample complexity of such methods, especially in continuous state spaces. This paper focuses on the simplest possible covering scheme (a discretized grid over the state space) and performs a sample-complexity comparison between the simplest (and previously commonly used) rollout sampling allocation strategy, which allocates samples equally at each state under consideration, and an almost as simple method, which allocates samples only as needed and requires significantly fewer samples.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4179
Deposited By:Christos Dimitrakakis
Deposited On:21 October 2008