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Published in:
2007 | OriginalPaper | Chapter
Reinforcement Learning for Cooperative Actions in a Partially Observable Multi-agent System
Authors : Yuki Taniguchi, Takeshi Mori, Shin Ishii
Published in: Artificial Neural Networks – ICANN 2007
Publisher: Springer Berlin Heidelberg
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In this article, we apply a policy gradient-based reinforcement learning to allowing multiple agents to perform cooperative actions in a partially observable environment. We introduce an auxiliary state variable, an internal state, whose stochastic process is Markov, for extracting important features of multi-agent’s dynamics. Computer simulations show that every agent can identify an appropriate internal state model and acquire a good policy; this approach is shown to be more effective than a traditional memory-based method.