Improvement of the performance using received message on learning of communication codes

Tatsuya Kasai, Hayato Kobayashi, Ayumi Shinohara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Communication is a key for facilitating multi-agent coordination on cooperative problems. On unknown problems, however, it is hard to construct beneficial communication codes. In order to tackle such problems, we focus on a method that allows agents to learn communication codes autonomously. Kasai et al. [2] proposed Signal Learning, by which agents learn policies of communication and action concurrently in multi-agent reinforcement learning framework. In this paper, we extend the existing signal learning and apply the extended method to an example problem, where agents can observe only partial information, for verifying the power of communication. We show that the performance of the proposed method is better than that of the existing method, and agents can obtain optimal policies on the applied problem by using the proposed method.

Original languageEnglish
Title of host publication8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1216-1217
Number of pages2
ISBN (Print)9781615673346
Publication statusPublished - 2009 Jan 1
Event8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009 - Budapest, Hungary
Duration: 2009 May 102009 May 15

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Other

Other8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009
CountryHungary
CityBudapest
Period09/5/1009/5/15

Keywords

  • Communication
  • Multi-agent reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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