Exploiting separability in multiagent planning with continuous-state mdps (extended abstract)
Identifieur interne : 000440 ( Main/Exploration ); précédent : 000439; suivant : 000441Exploiting separability in multiagent planning with continuous-state mdps (extended abstract)
Auteurs : Jilles Steeve Dibangoye [France] ; Christopher Amato [États-Unis] ; Olivier Buffet [France] ; François Charpillet [France]Source :
Abstract
Decentralized partially observable Markov deci- sion processes (Dec-POMDPs) provide a general model for decision-making under uncertainty in co- operative decentralized settings, but are difficult to solve optimally (NEXP-Complete). As a new way of solving these problems, we recently intro- duced a method for transforming a Dec-POMDP into a continuous-state deterministic MDP with a piecewise-linear and convex value function. This new Dec-POMDP formulation, which we call an occupancy MDP, allows powerful POMDP and continuous-state MDP methods to be used for the first time. However, scalability remains limited when the number of agents or problem variables becomes large. In this paper, we show that, un- der 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 be- tween agents, which can be exploited to improve performance. Unlike most previous methods, the novel continuous-state MDP algorithm retains op- timality 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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Grand Est</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/nancy</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></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-423090" type="direct"><org type="department" xml:id="struct-423090" status="VALID"><orgName>Department of Complex Systems, Artificial Intelligence & Robotics</orgName><orgName type="acronym">LORIA - AIS</orgName><desc><address><country key="FR"></country></address><ref type="url">http://www.loria.fr/la-recherche-en/departements/complex-system-and-artificial-intelligence</ref></desc><listRelation><relation active="#struct-206040" type="direct"></relation><relation active="#struct-300009" type="indirect"></relation><relation active="#struct-413289" type="indirect"></relation><relation name="UMR7503" active="#struct-441569" type="indirect"></relation></listRelation></org></tutelle><tutelle active="#struct-206040" type="indirect"><org type="laboratory" xml:id="struct-206040" status="VALID"><idno type="IdRef">067077927</idno><idno type="RNSR">198912571S</idno><idno type="IdUnivLorraine">[UL]RSI--</idno><orgName>Laboratoire Lorrain de Recherche en Informatique et ses Applications</orgName><orgName type="acronym">LORIA</orgName><date type="start">2012-01-01</date><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 active="#struct-300009" type="direct"></relation><relation active="#struct-413289" type="direct"></relation><relation name="UMR7503" active="#struct-441569" type="direct"></relation></listRelation></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - 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></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Decentralized partially observable Markov deci- sion processes (Dec-POMDPs) provide a general model for decision-making under uncertainty in co- operative decentralized settings, but are difficult to solve optimally (NEXP-Complete). As a new way of solving these problems, we recently intro- duced a method for transforming a Dec-POMDP into a continuous-state deterministic MDP with a piecewise-linear and convex value function. This new Dec-POMDP formulation, which we call an occupancy MDP, allows powerful POMDP and continuous-state MDP methods to be used for the first time. However, scalability remains limited when the number of agents or problem variables becomes large. In this paper, we show that, un- der 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 be- tween agents, which can be exploited to improve performance. Unlike most previous methods, the novel continuous-state MDP algorithm retains op- timality 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><affiliations><list><country><li>France</li><li>États-Unis</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Dibangoye, Jilles Steeve" sort="Dibangoye, Jilles Steeve" uniqKey="Dibangoye J" first="Jilles Steeve" last="Dibangoye">Jilles Steeve Dibangoye</name></noRegion><name sortKey="Buffet, Olivier" sort="Buffet, Olivier" uniqKey="Buffet O" first="Olivier" last="Buffet">Olivier Buffet</name><name sortKey="Charpillet, Francois" sort="Charpillet, Francois" uniqKey="Charpillet F" first="François" last="Charpillet">François Charpillet</name></country><country name="États-Unis"><noRegion><name sortKey="Amato, Christopher" sort="Amato, Christopher" uniqKey="Amato C" first="Christopher" last="Amato">Christopher Amato</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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