InforLorV4, Main, Exploration, bibRecord, 001F54

Reducing the dimentionality of the reward space in the Inverse Reinforcement Learning problem

Identifieur interne : 001F54 ( Main/Exploration ); précédent : 001F53; suivant : 001F55

Reducing the dimentionality of the reward space in the Inverse Reinforcement Learning problem

Auteurs : Edouard Klein [France] ; Matthieu Geist [France] ; Olivier Pietquin [France]

Source :

RBID : Hal:hal-00660612

Abstract

This paper deals with the Inverse Reinforcement Learning framework, whose purpose is to learn control policies from demonstrations by an expert. This method inferes from demonstrations a utility function the expert is allegedly maximizing. In this paper we map the reward space into a subset of smaller dimensionality without loss of generality for all Markov Decision Processes (MDPs). We then present three experimental results showing both the promising aspect of the application of this result to existing IRL methods and its shortcomings. We conclude with considerations on further research.

Url:

https://hal-supelec.archives-ouvertes.fr/hal-00660612

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper deals with the Inverse Reinforcement Learning framework, whose purpose is to learn control policies from demonstrations by an expert. This method inferes from demonstrations a utility function the expert is allegedly maximizing. In this paper we map the reward space into a subset of smaller dimensionality without loss of generality for all Markov Decision Processes (MDPs). We then present three experimental results showing both the promising aspect of the application of this result to existing IRL methods and its shortcomings. We conclude with considerations on further research.</div>
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Reducing the dimentionality of the reward space in the Inverse Reinforcement Learning problem

Reducing the dimentionality of the reward space in the Inverse Reinforcement Learning problem

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