Cooperative Co-Learning: A Model-Based Approach for Solving Multi Agent Reinforcement Problems

  • Authors:

  • Bruno Scherrer;François Charpillet

  • Affiliations:

  • -;-

  • Venue:
  • ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
  • Year:
  • 2002

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Abstract

Solving Multi-Agent 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 MarkovDecision Processes.