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Exploration in POMDPs
In
OGAI Journal (Oesterreichische Gesellschaft fuer Artificial Intelligence)
Vol.
1
No
1
De la page
24
A la page
31
Résumé
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.
Identifiants
Type de publication
journal article
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