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• W1782523431 abstract "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." @default.
• W1782523431 created "2016-06-24" @default.
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• W1782523431 date "2012-01-01" @default.
• W1782523431 modified "2023-09-25" @default.
• W1782523431 title "Active Learning of MDP Models" @default.
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• W1782523431 doi "https://doi.org/10.1007/978-3-642-29946-9_8" @default.
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