Contribution to the Control of a MAS’s Global Behaviour: Reinforcement Learning Tools
Identifieur interne : 003A54 ( Main/Merge ); précédent : 003A53; suivant : 003A55Contribution to the Control of a MAS’s Global Behaviour: Reinforcement Learning Tools
Auteurs : François Klein [France] ; Christine Bourjot [France] ; Vincent Chevrier [France]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
English descriptors
Abstract
Abstract: Reactive multi-agent systems present global behaviours uneasily linked to their local dynamics. When it comes to controlling such a system, usual analytical tools are difficult to use so specific techniques have to be engineered. We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. The behaviour of the whole system is controlled thanks to actions at different levels instead of building the behaviours of the agents, so that the complexity of the approach does not directly depend on the number of agents. The controllability is evaluated in terms of rate of convergence towards a target behaviour. We compare the results obtained on a toy example with the usual approach of parameter setting.
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DOI: 10.1007/978-3-642-02562-4_10
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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><idno type="DOI">10.1007/978-3-642-02562-4_10</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Control</term><term>MAS</term><term>emergence</term><term>experimental approach</term><term>global behaviour</term><term>reinforcement learning</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Reactive multi-agent systems present global behaviours uneasily linked to their local dynamics. When it comes to controlling such a system, usual analytical tools are difficult to use so specific techniques have to be engineered. We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. The behaviour of the whole system is controlled thanks to actions at different levels instead of building the behaviours of the agents, so that the complexity of the approach does not directly depend on the number of agents. The controllability is evaluated in terms of rate of convergence towards a target behaviour. We compare the results obtained on a toy example with the usual approach of parameter setting.</div></front></TEI></HAL><ISTEX><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Contribution to the Control of a MAS’s Global Behaviour: Reinforcement Learning Tools</title><author><name sortKey="Klein, Francois" sort="Klein, Francois" uniqKey="Klein F" first="François" last="Klein">François Klein</name></author><author><name sortKey="Bourjot, Christine" sort="Bourjot, Christine" uniqKey="Bourjot C" first="Christine" last="Bourjot">Christine Bourjot</name></author><author><name sortKey="Chevrier, Vincent" sort="Chevrier, Vincent" uniqKey="Chevrier V" first="Vincent" last="Chevrier">Vincent Chevrier</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:DDFCDA2D41606A621F882FFC3BDB100DF8D07B23</idno><date when="2009" year="2009">2009</date><idno type="doi">10.1007/978-3-642-02562-4_10</idno><idno type="url">https://api.istex.fr/ark:/67375/HCB-2WP12GMJ-3/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">003491</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">003491</idno><idno type="wicri:Area/Istex/Curation">003449</idno><idno type="wicri:Area/Istex/Checkpoint">000A83</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">000A83</idno><idno type="wicri:doubleKey">0302-9743:2009:Klein F:contribution:to:the</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Contribution to the Control of a MAS’s Global Behaviour: Reinforcement Learning Tools</title><author><name sortKey="Klein, Francois" sort="Klein, Francois" uniqKey="Klein F" first="François" last="Klein">François Klein</name><affiliation></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation></author><author><name sortKey="Bourjot, Christine" sort="Bourjot, Christine" uniqKey="Bourjot C" first="Christine" last="Bourjot">Christine Bourjot</name><affiliation></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation></author><author><name sortKey="Chevrier, Vincent" sort="Chevrier, Vincent" uniqKey="Chevrier V" first="Vincent" last="Chevrier">Vincent Chevrier</name><affiliation></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Reactive multi-agent systems present global behaviours uneasily linked to their local dynamics. When it comes to controlling such a system, usual analytical tools are difficult to use so specific techniques have to be engineered. We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. The behaviour of the whole system is controlled thanks to actions at different levels instead of building the behaviours of the agents, so that the complexity of the approach does not directly depend on the number of agents. The controllability is evaluated in terms of rate of convergence towards a target behaviour. We compare the results obtained on a toy example with the usual approach of parameter setting.</div></front></TEI></ISTEX></double></record>Pour manipuler ce document sous Unix (Dilib)
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