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Cooperative co-learning: a model-based approach for solving multi-agent reinforcement problems

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2 Author(s)
Scherrer, B. ; LORIA-INRIA Lorraine, Vandoeuvre-les-Nancy, France ; Charpillet, F.

Solving multiagent reinforcement learning problems is a key issue. Indeed, the complexity of deriving multiagent 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.

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Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. 14th IEEE International Conference on

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