Active Learning of MDP Models
Identifieur interne : 000A92 ( Hal/Curation ); précédent : 000A91; suivant : 000A93Active Learning of MDP Models
Auteurs : Mauricio Araya-L Pez [France] ; Olivier Buffet [France] ; Vincent Thomas [France] ; François Charpillet [France]Source :
Abstract
We consider the active learning problem of inferring the transition model of a Markov Decision Process by acting and observ- ing 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.
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<front><div type="abstract" xml:lang="en">We consider the active learning problem of inferring the transition model of a Markov Decision Process by acting and observ- ing 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.</div>
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<hal api="V3"><titleStmt><title xml:lang="en">Active Learning of MDP Models</title>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Active Learning of MDP Models</title>
<author role="aut"><persName><forename type="first">Mauricio</forename>
<surname>Araya-López</surname>
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<email>mauricio.araya@loria.fr</email>
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<author role="aut"><persName><forename type="first">Olivier</forename>
<surname>Buffet</surname>
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<email>olivier.buffet@loria.fr</email>
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<author role="aut"><persName><forename type="first">Vincent</forename>
<surname>Thomas</surname>
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<email>vthomas@loria.fr</email>
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<author role="aut"><persName><forename type="first">François</forename>
<surname>Charpillet</surname>
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<email>francois.charpillet@loria.fr</email>
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<monogr><meeting><title>European Workshop On Reinforcement Learning</title>
<date type="start">2011-09-09</date>
<date type="end">2011-09-11</date>
<settlement>Athène</settlement>
<country key="GR">Greece</country>
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<imprint><date type="datePub">2011-09</date>
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<profileDesc><langUsage><language ident="en">English</language>
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<abstract xml:lang="en">We consider the active learning problem of inferring the transition model of a Markov Decision Process by acting and observ- ing 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.</abstract>
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