Cooperative Co-learning: A Model-based Approach for Solving Multi Agent Reinforcement Problems

Bruno Scherrer 1 François Charpillet 2
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Solving Multi-Agent Reinforcement Learning Problems is a key issue. Indeed, the complexity of deriving multi-agent plans, especially when one uses an explicit model of the problem, is dramatically increasing with the number of agents. This papers introduces a general iterative heuristic: at each step one chooses a sub-group of agents and update their policies to optimize the task given the rest of agents have fixed plans. We analyse this process in a general purpose and show how it can be applied to Markov Decision Processes, Partially Observable Markov Decision Processes and Decentralized Partially Observable Markov Decision Processes.
Type de document :
Communication dans un congrès
14th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2002, 2002, Washington, USA, IEEE, 6 p, 2002
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Soumis le : mardi 26 septembre 2006 - 14:51:12
Dernière modification le : jeudi 11 janvier 2018 - 06:19:50

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  • HAL Id : inria-00100814, version 1

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Bruno Scherrer, François Charpillet. Cooperative Co-learning: A Model-based Approach for Solving Multi Agent Reinforcement Problems. 14th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2002, 2002, Washington, USA, IEEE, 6 p, 2002. 〈inria-00100814〉

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