Adaptive Combination of Behaviors in an Agent
Identifieur interne : 009124 ( Main/Merge ); précédent : 009123; suivant : 009125Adaptive Combination of Behaviors in an Agent
Auteurs : Olivier Buffet [France] ; Alain Dutech [France] ; François Charpillet [France]Source :
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Abstract
Agents are of interest mainly when confronted with complex tasks. We propose a methodology for the automated design of such agents (in the framework of Markov Decision Processes) in the case where the global task can be decomposed into simpler -possibly concurrent- sub-tasks. This is accomplished by automatically combining basic behaviors using Reinforcement Learning methods. \correction[Basic behaviors]{The main idea is to build a global policy using a weighted combination of basic policies, the weights being learned by the agent (using Simulated Annealing in our case). These basic behaviors} can either be learned or reused from previous tasks since they will not need to be tuned to the new task. Furthermore, the agents designed by our methodology are highly scalable as, without further refinement of the global behavior, they can automatically combine several instances of the same basic behavior to take into account concurrent occurences of the same subtask.
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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><author><name sortKey="Charpillet, Francois" sort="Charpillet, Francois" uniqKey="Charpillet F" first="François" last="Charpillet">François Charpillet</name><affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-2355" status="OLD"><idno type="RNSR">200218290B</idno><orgName>Autonomous intelligent machine</orgName><orgName type="acronym">MAIA</orgName><desc><address><country key="FR"></country></address><ref type="url">http://www.inria.fr/equipes/maia</ref></desc><listRelation><relation active="#struct-160" type="direct"></relation><relation name="UMR7503" active="#struct-441569" type="indirect"></relation><relation active="#struct-300009" type="indirect"></relation><relation active="#struct-300291" type="indirect"></relation><relation active="#struct-300292" type="indirect"></relation><relation active="#struct-300293" type="indirect"></relation><relation active="#struct-2496" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-160" type="direct"><org type="laboratory" xml:id="struct-160" status="OLD"><orgName>Laboratoire Lorrain de Recherche en Informatique et ses Applications</orgName><orgName type="acronym">LORIA</orgName><desc><address><addrLine>Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.loria.fr</ref></desc><listRelation><relation name="UMR7503" active="#struct-441569" type="direct"></relation><relation active="#struct-300009" type="direct"></relation><relation active="#struct-300291" type="direct"></relation><relation active="#struct-300292" type="direct"></relation><relation active="#struct-300293" type="direct"></relation></listRelation></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</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-300009" type="indirect"><org type="institution" xml:id="struct-300009" status="VALID"><orgName>Institut National de Recherche en Informatique et en Automatique</orgName><orgName 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>adaptation</term><term>apprentissage par renforcement</term><term>mise à l'échelle</term><term>reinforcement learning</term><term>scalability</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Agents are of interest mainly when confronted with complex tasks. We propose a methodology for the automated design of such agents (in the framework of Markov Decision Processes) in the case where the global task can be decomposed into simpler -possibly concurrent- sub-tasks. This is accomplished by automatically combining basic behaviors using Reinforcement Learning methods. \correction[Basic behaviors]{The main idea is to build a global policy using a weighted combination of basic policies, the weights being learned by the agent (using Simulated Annealing in our case). These basic behaviors} can either be learned or reused from previous tasks since they will not need to be tuned to the new task. Furthermore, the agents designed by our methodology are highly scalable as, without further refinement of the global behavior, they can automatically combine several instances of the same basic behavior to take into account concurrent occurences of the same subtask.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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