Batch, Off-policy and Model-free Apprenticeship Learning
Identifieur interne : 002141 ( Main/Exploration ); précédent : 002140; suivant : 002142Batch, Off-policy and Model-free Apprenticeship Learning
Auteurs : Edouard Klein [France] ; Matthieu Geist [France] ; Olivier Pietquin [France]Source :
Abstract
This paper addresses the problem of apprenticeship learning, that is learning control policies from demonstration by an expert. An efficient framework for it is inverse reinforcement learning (IRL). Based on the assumption that the expert maximizes a utility function, IRL aims at learning the underlying reward from example trajectories. Many IRL algorithms assume that the reward function is linearly parameterized and rely on the computation of some associated feature expectations, which is done through Monte Carlo simulation. However, this assumes to have full trajectories for the expert policy as well as at least a generative model for intermediate policies. In this paper, we introduce a temporal difference method, namely LSTD-mu, to compute these feature expectations. This allows extending apprenticeship learning to a batch and off-policy setting.
Url:
Affiliations:
- France
- Franche-Comté, Grand Est, Lorraine (région)
- Besançon, Metz, Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université Paul Verlaine - Metz, Université de Bourgogne Franche-Comté, Université de Franche-Comté, Université de Lorraine
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Le document en format XML
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Metz</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Pietquin, Olivier" sort="Pietquin, Olivier" uniqKey="Pietquin O" first="Olivier" last="Pietquin">Olivier Pietquin</name><affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-394500" status="INCOMING"><orgName>IMS - Equipe Information, Multimodalité et Signal</orgName><desc><address><country key="FR"></country></address></desc><listRelation><relation active="#struct-24541" type="direct"></relation><relation active="#struct-242365" type="indirect"></relation><relation active="#struct-411575" type="indirect"></relation><relation active="#struct-301991" type="indirect"></relation><relation active="#struct-301990" type="indirect"></relation><relation active="#struct-300812" type="indirect"></relation><relation active="#struct-300413" type="indirect"></relation><relation active="#struct-300289" type="indirect"></relation><relation name="UMI2958" active="#struct-441569" type="indirect"></relation><relation active="#struct-26305" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-24541" type="direct"><org type="laboratory" xml:id="struct-24541" status="VALID"><idno type="RNSR">200619366D</idno><orgName>Georgia Tech - 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Metz</orgName><orgName type="acronym">UPVM</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle name="UMI2958" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="IdRef">02636817X</idno><idno type="ISNI">0000000122597504</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle><tutelle active="#struct-26305" type="direct"><org type="laboratory" xml:id="struct-26305" status="VALID"><orgName>SUPELEC-Campus Metz</orgName><desc><address><addrLine>2 rue Edouard Belin 57070 Metz</addrLine><country key="FR"></country></address><ref type="url">http://www.metz.supelec.fr/metz/</ref></desc><listRelation><relation active="#struct-300812" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city" wicri:auto="siege">Besançon</settlement><region type="region" nuts="2">Franche-Comté</region></placeName><orgName type="university">Université de Franche-Comté</orgName><orgName type="institution" wicri:auto="newGroup">Université de Bourgogne Franche-Comté</orgName><placeName><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Paul Verlaine - Metz</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper addresses the problem of apprenticeship learning, that is learning control policies from demonstration by an expert. An efficient framework for it is inverse reinforcement learning (IRL). Based on the assumption that the expert maximizes a utility function, IRL aims at learning the underlying reward from example trajectories. Many IRL algorithms assume that the reward function is linearly parameterized and rely on the computation of some associated feature expectations, which is done through Monte Carlo simulation. However, this assumes to have full trajectories for the expert policy as well as at least a generative model for intermediate policies. In this paper, we introduce a temporal difference method, namely LSTD-mu, to compute these feature expectations. This allows extending apprenticeship learning to a batch and off-policy setting.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Franche-Comté</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Besançon</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université Paul Verlaine - Metz</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Franche-Comté</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Klein, Edouard" sort="Klein, Edouard" uniqKey="Klein E" first="Edouard" last="Klein">Edouard Klein</name></region><name sortKey="Geist, Matthieu" sort="Geist, Matthieu" uniqKey="Geist M" first="Matthieu" last="Geist">Matthieu Geist</name><name sortKey="Pietquin, Olivier" sort="Pietquin, Olivier" uniqKey="Pietquin O" first="Olivier" last="Pietquin">Olivier Pietquin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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