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Shaping Multi-Agent Systems with Gradient Reinforcement Learning
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.
Cited literature [49 references]
https://hal.inria.fr/inria-00118983
Contributor : Alain Dutech <>
Submitted on : Friday, December 8, 2006 - 1:29:38 PM
Last modification on : Friday, February 26, 2021 - 3:28:04 PM
Long-term archiving on: : Wednesday, April 7, 2010 - 12:02:12 AM
Contributor : Alain Dutech <>
Submitted on : Friday, December 8, 2006 - 1:29:38 PM
Last modification on : Friday, February 26, 2021 - 3:28:04 PM
Long-term archiving on: : Wednesday, April 7, 2010 - 12:02:12 AM
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