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Journal Articles Autonomous Agents and Multi-Agent Systems Year : 2007

Shaping Multi-Agent Systems with Gradient Reinforcement Learning


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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inria-00118983 , version 1 (08-12-2006)



Olivier Buffet, Alain Dutech, François Charpillet. Shaping Multi-Agent Systems with Gradient Reinforcement Learning. Autonomous Agents and Multi-Agent Systems, 2007, 15 (2), pp.197--220. ⟨10.1007/s10458-006-9010-5⟩. ⟨inria-00118983⟩
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