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Title:
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USING SOCIAL ACTIONS AND RL-ALGORITHMS TO BUILD POLICIES IN DEC-POMDP |
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Author(s):
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Thomas Vincent , Akplogan Mahuna |
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ISBN:
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978-972-8924-87-4 |
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Editors:
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António Palma dos Reis |
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Year:
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2009 |
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Edition:
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Single |
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Keywords:
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Multi-agent systems, Markov decision processes, reinforcement learning, interaction |
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Type:
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Full Paper |
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First Page:
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35 |
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Last Page:
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42 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Building individual behaviors to solve collective problems is a major stake whose applications are found in several
domains. Dec-POMDP 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 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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