PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exploration in POMDPs
Christos Dimitrakakis
Österreichische Gesellschaft für Artificial Intelligence Journal Volume 1, pp. 24-31, 2008.

Abstract

In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving known partially-observable Markov decision processes (POMDPs) have been proposed. In this paper we review the similarities and differences between those two domains and propose methods to deal with them simultaneously. This enables us to attack the Bayes-optimal reinforcement learning problem in POMDPs.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4180
Deposited By:Christos Dimitrakakis
Deposited On:21 October 2008