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. DecPOMDP has been proposed as 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 DecPOMDP and we proposed three decentralized reinforcement learning algorithms which approximate the optimal policy in DecPOMDP. This article analyses the results obtained and argues that this new approach seems promising for automatic top-down collective behavior computation.
Type de document :
Communication dans un congrès
IADIS International Conference on Intelligent Systems and Agents 2009 - IADIS ISA 2009, Jun 2009, Lagoa, Portugal. 2009
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https://hal.inria.fr/inria-00399400
Contributeur : Vincent Thomas <>
Soumis le : vendredi 26 juin 2009 - 12:01:09
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51

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  • HAL Id : inria-00399400, version 1

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Vincent Thomas, Mahuna Akplogan. Using "social actions" and RL algorithms to build policies in Dec-POMDP. IADIS International Conference on Intelligent Systems and Agents 2009 - IADIS ISA 2009, Jun 2009, Lagoa, Portugal. 2009. 〈inria-00399400〉

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