Signal Learning with Messages by Reinforcement Learning in Multi-agent Pursuit Problem

Communication is a key for facilitating multi-agent coordination on cooperative problems. Reinforcement learning is one of the learning methods for such cooperative behavior of agents. Kasai et al. proposed Signal Learning (SL) and Signal Learning with Messages (SLM) by which agents learn policies o...

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Bibliographic Details
Published in:Procedia computer science 2014, Vol.35, p.233-240
Main Authors: Noro, Kozue, Tenmoto, Hiroshi, Kamiya, Akimoto
Format: Article
Language:English
Subjects:
Agents communication
Multi-agent system
Reinforcement learning
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Summary:Communication is a key for facilitating multi-agent coordination on cooperative problems. Reinforcement learning is one of the learning methods for such cooperative behavior of agents. Kasai et al. proposed Signal Learning (SL) and Signal Learning with Messages (SLM) by which agents learn policies of communication and action concurrently in multi-agent reinforcement learning framework. In this study, we experimented that the performance of the SLM is better than SL to pursuit problem where agents can observe only partial information and can move four directions. As a result, it has been shown that learning performance in SLM with longer messages is better than SL.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2014.08.103