Using “Social actions” and RL-algorithms to build policies in DEC-POMDP

Vincent Thomas 1 Mahuna Akplogan 2
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Building individual behaviors to solve collective problems is a major stake whose applications are found in several domains. To do so, Dec-POMDP has been proposed as a formalism for describing multi-agent problems. However, solving a Dec-POMDP turned out to be a NEXP problem. In this study, we introduced the original concept of social action to get round the inherent complexity of Dec-POMDP and we proposed three decentralized reinforcement learning algorithms which approximate the optimal policy in Dec-POMDP. This article analyses the results obtained and argues that this new approach seems promising for automatic top-down collective behavior computation.
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Article dans une revue
IADIS International Journal on Computer Science and Information Systems, IADIS, 2009, 4 (3), pp.82-98. 〈http://www.iadis.org/ijcsis/vol4_numb3.asp〉
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Contributeur : Vincent Thomas <>
Soumis le : mercredi 17 novembre 2010 - 09:03:22
Dernière modification le : mardi 18 septembre 2018 - 14:04:02

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  • HAL Id : inria-00536851, version 1

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Vincent Thomas, Mahuna Akplogan. Using “Social actions” and RL-algorithms to build policies in DEC-POMDP. IADIS International Journal on Computer Science and Information Systems, IADIS, 2009, 4 (3), pp.82-98. 〈http://www.iadis.org/ijcsis/vol4_numb3.asp〉. 〈inria-00536851〉

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