Presentation 2013-11-28
Efficient Channel Division and Information Sharing Using Reinforcement Learning for Cooperative Multi-agent Systems
Xue ZHANG, Toshiharu SUGAWARA,
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Abstract(in English) We propose a method that teams of software agents have been increasingly used in sophisticated real-world systems. In these teams, agents typically need to communicate with each other to cooperate for solving a problem. However, the communication mediums are highly restricted making it infeasible for every agent to broadcast all relevant information to each other. For realizing efficient cooperative actions, not only fair usage of communication bandwidth, but also sharing communication media/channels with collaborating agents is required. To solve this problem, we propose a decentralized algorithm by introducing the two steps of channel division and information sharing with the purpose of maximizing the utility of the whole system. This combines multi-agent learning techniques with fast decision-theoretic reasoning mechanisms to predict the impact that a single agent has on the entire system. We present that our method outperforms the conventional decentralized method by 7.54%, and the results show that our algorithm is more efficient.
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Keyword(in English) Reinforcement learning / Teamwork / Communication / Cooperative agent
Paper # AI2013-21
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Conference Information
Committee AI
Conference Date 2013/11/21(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Efficient Channel Division and Information Sharing Using Reinforcement Learning for Cooperative Multi-agent Systems
Sub Title (in English)
Keyword(1) Reinforcement learning
Keyword(2) Teamwork
Keyword(3) Communication
Keyword(4) Cooperative agent
1st Author's Name Xue ZHANG
1st Author's Affiliation School of Fundamental Science and Engineering, Waseda University()
2nd Author's Name Toshiharu SUGAWARA
2nd Author's Affiliation School of Fundamental Science and Engineering, Waseda University
Date 2013-11-28
Paper # AI2013-21
Volume (vol) vol.113
Number (no) 332
Page pp.pp.-
#Pages 6
Date of Issue