Contribution to the Control of a MAS’s Global Behaviour: Reinforcement Learning Tools
Identifieur interne : 003449 ( Istex/Curation ); précédent : 003448; suivant : 003450Contribution 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 ]
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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<record><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><affiliation><mods:affiliation>LORIA – Nancy University, Campus scientifique BP 239, 54506, Vandoeuvre-lès-Nancy Cedex</mods:affiliation><wicri:noCountry code="subField">Cedex</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: Francois.Klein@loria.fr</mods:affiliation><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><mods:affiliation>LORIA – Nancy University, Campus scientifique BP 239, 54506, Vandoeuvre-lès-Nancy Cedex</mods:affiliation><wicri:noCountry code="subField">Cedex</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: Christine.Bourjot@loria.fr</mods:affiliation><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><mods:affiliation>LORIA – Nancy University, Campus scientifique BP 239, 54506, Vandoeuvre-lès-Nancy Cedex</mods:affiliation><wicri:noCountry code="subField">Cedex</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: Vincent.Chevrier@loria.fr</mods:affiliation><country wicri:rule="url">France</country></affiliation></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></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><mods:affiliation>LORIA – Nancy University, Campus scientifique BP 239, 54506, Vandoeuvre-lès-Nancy Cedex</mods:affiliation></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: Francois.Klein@loria.fr</mods:affiliation><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><mods:affiliation>LORIA – Nancy University, Campus scientifique BP 239, 54506, Vandoeuvre-lès-Nancy Cedex</mods:affiliation></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: Christine.Bourjot@loria.fr</mods:affiliation><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><mods:affiliation>LORIA – Nancy University, Campus scientifique BP 239, 54506, Vandoeuvre-lès-Nancy Cedex</mods:affiliation></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: Vincent.Chevrier@loria.fr</mods:affiliation><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></record>Pour manipuler ce document sous Unix (Dilib)
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