Exploiting Separability in Multiagent Planning with Continuous-State MDPs
Identifieur interne : 000B23 ( Hal/Checkpoint ); précédent : 000B22; suivant : 000B24Exploiting Separability in Multiagent Planning with Continuous-State MDPs
Auteurs : Jilles Steeve Dibangoye [France] ; Christopher Amato [États-Unis] ; Olivier Buffet [France] ; François Charpillet [France]Source :
English descriptors
Abstract
Recent years have seen significant advances in techniques for optimally solving multiagent problems represented as decentralized partially observable Markov decision processes (Dec-POMDPs). A new method achieves scalability gains by converting Dec-POMDPs into continuous state MDPs. This method relies on the assumption of a centralized planning phase that generates a set of decentralized policies for the agents to execute. However, scalability remains limited when the number of agents or problem variables becomes large. In this paper, we show that, under certain separability conditions of the optimal value function, the scalability of this approach can increase considerably. This separability is present when there is locality of interaction, which — as other approaches (such as those based on the ND-POMDP subclass) have already shown — can be exploited to improve performance. Unlike most previous methods, the novel continuous-state MDP algorithm retains optimality and convergence guarantees. Results show that the extension using separability can scale to a large number of agents and domain variables while maintaining optimality.
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CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></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></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><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 de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>ND-POMDPs</term><term>Planning under uncertainty</term><term>cooperative multiagent systems</term><term>decentralized POMDPs</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Recent years have seen significant advances in techniques for optimally solving multiagent problems represented as decentralized partially observable Markov decision processes (Dec-POMDPs). A new method achieves scalability gains by converting Dec-POMDPs into continuous state MDPs. This method relies on the assumption of a centralized planning phase that generates a set of decentralized policies for the agents to execute. However, scalability remains limited when the number of agents or problem variables becomes large. In this paper, we show that, under certain separability conditions of the optimal value function, the scalability of this approach can increase considerably. This separability is present when there is locality of interaction, which — as other approaches (such as those based on the ND-POMDP subclass) have already shown — can be exploited to improve performance. Unlike most previous methods, the novel continuous-state MDP algorithm retains optimality and convergence guarantees. Results show that the extension using separability can scale to a large number of agents and domain variables while maintaining optimality.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Exploiting Separability in Multiagent Planning with Continuous-State MDPs</title><author role="crp"><persName><forename type="first">Jilles Steeve</forename><surname>Dibangoye</surname></persName><email>jilles-steeve.dibangoye@insa-lyon.fr</email><ptr type="url" target="http://jilles.dibangoye.net/"></ptr><idno type="idhal">jilles-steeve-dibangoye</idno><idno type="halauthor">1123509</idno><orgName ref="#struct-219748"></orgName><affiliation ref="#struct-205124"></affiliation></author><author role="aut"><persName><forename type="first">Christopher</forename><surname>Amato</surname></persName><email>camato@csail.mit.edu</email><idno type="halauthor">1105148</idno><orgName ref="#struct-22441"></orgName><affiliation ref="#struct-406632"></affiliation></author><author role="crp"><persName><forename type="first">Olivier</forename><surname>Buffet</surname></persName><email>olivier.buffet@loria.fr</email><ptr type="url" target="http://www.loria.fr/~buffet/"></ptr><idno type="halauthor">1105149</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-205124"></affiliation></author><author role="aut"><persName><forename type="first">François</forename><surname>Charpillet</surname></persName><email>francois.charpillet@inria.fr</email><ptr type="url" target="http://www.loria.fr/~charp/"></ptr><idno type="idhal">francois-charpillet</idno><idno type="halauthor">1105150</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-205124"></affiliation></author><editor role="depositor"><persName><forename>Olivier</forename><surname>Buffet</surname></persName><email>olivier.buffet@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2014-12-10 15:32:32</date><date type="whenModified">2015-09-22 01:12:21</date><date type="whenReleased">2014-12-18 14:30:52</date><date type="whenProduced">2014-05-05</date><date type="whenEndEmbargoed">2014-12-10</date><ref type="file" target="https://hal.inria.fr/hal-01092066/document"><date notBefore="2014-12-10"></date></ref><ref type="file" subtype="greenPublisher" n="1" target="https://hal.inria.fr/hal-01092066/file/aamas14a.pdf"><date notBefore="2014-12-10"></date></ref></edition><respStmt><resp>contributor</resp><name key="103770"><persName><forename>Olivier</forename><surname>Buffet</surname></persName><email>olivier.buffet@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01092066</idno><idno type="halUri">https://hal.inria.fr/hal-01092066</idno><idno type="halBibtex">dibangoye:hal-01092066</idno><idno type="halRefHtml">Alessio Lomuscio; Paul Scerri; Ana Bazzan; Michael Huhns. 13th International Conference on Autonomous Agents and Multiagent Systems, May 2014, Paris, France. ACM, 2014, Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems. <http://aamas2014.lip6.fr></idno><idno type="halRef">Alessio Lomuscio; Paul Scerri; Ana Bazzan; Michael Huhns. 13th International Conference on Autonomous Agents and Multiagent Systems, May 2014, Paris, France. ACM, 2014, Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems. <http://aamas2014.lip6.fr></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Exploiting Separability in Multiagent Planning with Continuous-State MDPs</title><author role="crp"><persName><forename type="first">Jilles Steeve</forename><surname>Dibangoye</surname></persName><email>jilles-steeve.dibangoye@insa-lyon.fr</email><ptr type="url" target="http://jilles.dibangoye.net/"></ptr><idno type="idHal">jilles-steeve-dibangoye</idno><idno type="halAuthorId">1123509</idno><orgName ref="#struct-219748"></orgName><affiliation ref="#struct-205124"></affiliation></author><author role="aut"><persName><forename type="first">Christopher</forename><surname>Amato</surname></persName><email>camato@csail.mit.edu</email><idno type="halAuthorId">1105148</idno><orgName ref="#struct-22441"></orgName><affiliation ref="#struct-406632"></affiliation></author><author role="crp"><persName><forename type="first">Olivier</forename><surname>Buffet</surname></persName><email>olivier.buffet@loria.fr</email><ptr type="url" target="http://www.loria.fr/~buffet/"></ptr><idno type="halAuthorId">1105149</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-205124"></affiliation></author><author role="aut"><persName><forename type="first">François</forename><surname>Charpillet</surname></persName><email>francois.charpillet@inria.fr</email><ptr type="url" target="http://www.loria.fr/~charp/"></ptr><idno type="idHal">francois-charpillet</idno><idno type="halAuthorId">1105150</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-205124"></affiliation></author></analytic><monogr><meeting><title>13th International Conference on Autonomous Agents and Multiagent Systems</title><date type="start">2014-05-05</date><date type="end">2014-05-09</date><settlement>Paris</settlement><country key="FR">France</country></meeting><editor>Alessio Lomuscio</editor><editor>Paul Scerri</editor><editor>Ana Bazzan</editor><editor>Michael Huhns</editor><imprint><publisher>ACM</publisher><biblScope unit="serie">Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems</biblScope><date type="datePub">2014-05</date></imprint></monogr><ref type="publisher">http://aamas2014.lip6.fr</ref></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Planning under uncertainty</term><term xml:lang="en">cooperative multiagent systems</term><term xml:lang="en">decentralized POMDPs</term><term xml:lang="en">ND-POMDPs</term></keywords><classCode scheme="halDomain" n="info.info-ai">Computer Science [cs]/Artificial Intelligence [cs.AI]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">Recent years have seen significant advances in techniques for optimally solving multiagent problems represented as decentralized partially observable Markov decision processes (Dec-POMDPs). A new method achieves scalability gains by converting Dec-POMDPs into continuous state MDPs. This method relies on the assumption of a centralized planning phase that generates a set of decentralized policies for the agents to execute. However, scalability remains limited when the number of agents or problem variables becomes large. In this paper, we show that, under certain separability conditions of the optimal value function, the scalability of this approach can increase considerably. This separability is present when there is locality of interaction, which — as other approaches (such as those based on the ND-POMDP subclass) have already shown — can be exploited to improve performance. Unlike most previous methods, the novel continuous-state MDP algorithm retains optimality and convergence guarantees. Results show that the extension using separability can scale to a large number of agents and domain variables while maintaining optimality.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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