Etude de différentes combinaisons de comportements adaptatives au sein d'un agent.
Identifieur interne : 008107 ( Main/Merge ); précédent : 008106; suivant : 008108Etude de différentes combinaisons de comportements adaptatives au sein d'un agent.
Auteurs : Olivier Buffet [France] ; Alain Dutech [France] ; François Charpillet [France]Source :
Descripteurs français
- mix :
Abstract
Les travaux présentés ici s'intéressent à la synthèse automatique d'agents en environnement incertain. Nous avons ainsi fait le choix de nous placer dans le cadre de l'apprentissage par renforcement (A/R), et plus précisément des processus de décision markoviens partiellement observables (PDMPO). Une seconde décision a été de se restreindre à des agents travaillant sans modèle (de leur environnement) ni mémoire à court terme. Par contre, nous nous sommes particulièrement intéressés au cas d'agents confrontés à de multiples motivations (objectifs) simultanées. Cette problématique précise conduit assez directement aux travaux du domaine de la sélection d'action (S/A). Le présent article propose et évalue différentes architectures de sélection d'action pour le contrôle d'agents. Leur principe commun est de combiner de manière adaptative des comportements de base déjà connus, en apprenant (par renforcement) les réglages de la combinaison de manière à maximiser les gains de l'agent. Ces travaux ouvrent la voix vers une seconde étape dans laquelle les comportements de base eux-mêmes seraient sélectionnés et conçus de manière automatique.
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Hal:inria-00099773Le document en format XML
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type="acronym">Inria</orgName><desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>action selection</term><term>apprentissage par renforcement</term><term>markov decision processes</term><term>motivations multiples</term><term>multiple motivations</term><term>processus de décision markoviens</term><term>reinforcement learning</term><term>sélection d'action</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">Les travaux présentés ici s'intéressent à la synthèse automatique d'agents en environnement incertain. Nous avons ainsi fait le choix de nous placer dans le cadre de l'apprentissage par renforcement (A/R), et plus précisément des processus de décision markoviens partiellement observables (PDMPO). Une seconde décision a été de se restreindre à des agents travaillant sans modèle (de leur environnement) ni mémoire à court terme. Par contre, nous nous sommes particulièrement intéressés au cas d'agents confrontés à de multiples motivations (objectifs) simultanées. Cette problématique précise conduit assez directement aux travaux du domaine de la sélection d'action (S/A). Le présent article propose et évalue différentes architectures de sélection d'action pour le contrôle d'agents. Leur principe commun est de combiner de manière adaptative des comportements de base déjà connus, en apprenant (par renforcement) les réglages de la combinaison de manière à maximiser les gains de l'agent. Ces travaux ouvrent la voix vers une seconde étape dans laquelle les comportements de base eux-mêmes seraient sélectionnés et conçus de manière automatique.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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