Apprentissage off-policy appliqué à un système de dialogue basé sur les PDMPO
Identifieur interne : 001C55 ( Main/Exploration ); précédent : 001C54; suivant : 001C56Apprentissage off-policy appliqué à un système de dialogue basé sur les PDMPO
Auteurs : Lucie Daubigney [France] ; Matthieu Geist [France] ; Olivier Pietquin [France]Source :
Abstract
L'apprentissage par renforcement (AR) fait maintenant partie de l'état de l'art dans le domaine de l'optimisation de système de dialogues vocaux. La plupart des méthodes appliquées aux systèmes de dialogue basées sur l'AR, comme par exemple celles qui utilisent des processus gaussiens, requièrent de tester des changements plus ou moins aléatoires dans la politique. Cette manière de procéder est appelée apprentissage " onpolicy ". Néanmoins, celle-ci peut induire des comportements de la part du système incohérents aux yeux de l'utilisateur. Les algorithmes devraient idéalement trouver la politique optimale d'après l'observation d'interactions générées par une politique sous-optimale mais proposant un comportement cohérent à l'utilisateur : c'est l'apprentissage " off-policy ". Dans cette contribution, un algorithme efficace sur les échantillons permettant l'apprentissage off-policy et en ligne la politique optimale est proposé. Cet algorithme combiné à une représentation compacte, non-linéaire de la fonction de valeur (un perceptron multicouche) permet de gérer des systèmes à grande échelle.
Url:
Affiliations:
- France
- Franche-Comté, Grand Est, Lorraine (région)
- Besançon, Metz, Nancy
- Université Paul Verlaine - Metz, Université de Bourgogne Franche-Comté, Université de Franche-Comté, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 005687
- to stream Hal, to step Curation: 005687
- to stream Hal, to step Checkpoint: 001882
- to stream Main, to step Merge: 001C87
- to stream Main, to step Curation: 001C55
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="fr">L'apprentissage par renforcement (AR) fait maintenant partie de l'état de l'art dans le domaine de l'optimisation de système de dialogues vocaux. La plupart des méthodes appliquées aux systèmes de dialogue basées sur l'AR, comme par exemple celles qui utilisent des processus gaussiens, requièrent de tester des changements plus ou moins aléatoires dans la politique. Cette manière de procéder est appelée apprentissage " onpolicy ". Néanmoins, celle-ci peut induire des comportements de la part du système incohérents aux yeux de l'utilisateur. Les algorithmes devraient idéalement trouver la politique optimale d'après l'observation d'interactions générées par une politique sous-optimale mais proposant un comportement cohérent à l'utilisateur : c'est l'apprentissage " off-policy ". Dans cette contribution, un algorithme efficace sur les échantillons permettant l'apprentissage off-policy et en ligne la politique optimale est proposé. Cet algorithme combiné à une représentation compacte, non-linéaire de la fonction de valeur (un perceptron multicouche) permet de gérer des systèmes à grande échelle.</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>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="Daubigney, Lucie" sort="Daubigney, Lucie" uniqKey="Daubigney L" first="Lucie" last="Daubigney">Lucie Daubigney</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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