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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Active Learning of MDP Models
verfasst von : Mauricio Araya-López, Olivier Buffet, Vincent Thomas, François Charpillet
Erschienen in: Recent Advances in Reinforcement Learning
Verlag: Springer Berlin Heidelberg
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We consider the active learning problem of inferring the transition model of a Markov Decision Process by acting and observing transitions. This is particularly useful when no reward function is
a priori
defined. Our proposal is to cast the active learning task as a utility maximization problem using Bayesian reinforcement learning with belief-dependent rewards. After presenting three possible performance criteria, we derive from them the belief-dependent rewards to be used in the decision-making process. As computing the optimal Bayesian value function is intractable for large horizons, we use a simple algorithm to approximately solve this optimization problem. Despite the sub-optimality of this technique, we show experimentally that our proposal is efficient in a number of domains.