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Coordination through Mutual Notification in Cooperative Multiagent Reinforcement Learning

Daniel Szer 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We present a new algorithm for cooperative reinforcement learning in multiagent systems. Our main concern is the correct coordination between the members of the team: We seek to obtain an optimal solution for the team as a whole while keeping the learning as much decentralized as possible. We furthermore consider autonomous and independently learning agents that do not store any explicit information about their teammates' behavior. Reward functions may be different for each agent and coordination between agents occurs through communication, namely the mutual notification algorithm. We define the learning problem as a decentralized MDP, we then give an optimality criterion, and proove the convergence of the algorithm for deterministic environments. Finally we study the convergence properties and communication overhead on two small examples.
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Submitted on : Tuesday, September 26, 2006 - 10:15:38 AM
Last modification on : Wednesday, February 2, 2022 - 3:51:47 PM


  • HAL Id : inria-00100215, version 1



Daniel Szer, François Charpillet. Coordination through Mutual Notification in Cooperative Multiagent Reinforcement Learning. [Intern report] A04-R-051 || szer04a, 2004, 8 p. ⟨inria-00100215⟩



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