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Shaping Multi-Agent Systems with Gradient Reinforcement Learning

Olivier Buffet 1 Alain Dutech 2 François Charpillet 2
2 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : An original Reinforcement Learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task of automatically building a coordinated system is of crucial importance. To that end, we design simple reactive agents in a decentralized way as independent learners. But to cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face a sequence of progressively more complex tasks. We illustrate this general framework by computer experiments where agents have to coordinate to reach a global goal.
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Submitted on : Friday, December 8, 2006 - 1:29:38 PM
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Olivier Buffet, Alain Dutech, François Charpillet. Shaping Multi-Agent Systems with Gradient Reinforcement Learning. Autonomous Agents and Multi-Agent Systems, Springer Verlag, 2007, 15 (2), pp.197--220. ⟨10.1007/s10458-006-9010-5⟩. ⟨inria-00118983⟩

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