Maintaining System State Information in a Multiagent Environment for Effective Learning
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- CHEN Gang
- Information Communication Institute of Singapore, School of Electrical and Electronic Engineering, Nanyang Technological University
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- YANG Zhonghua
- Information Communication Institute of Singapore, School of Electrical and Electronic Engineering, Nanyang Technological University
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- HE Hao
- Singapore Institute of Manufacturing Technology
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- GOH Kiah-Mok
- Singapore Institute of Manufacturing Technology
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説明
One fundamental issue in multiagent reinforcement learning is how to deal with the limited local knowledge of an agent in order to achieve effective learning. In this paper, we argue that this issue can be more effectively solved if agents are equipped with a consistent global view. We achieve this by requiring agents to follow an interacting protocol. The properties of the protocol are derived and theoretically analyzed. A distributed protocol that satisfies these properties is presented. The experimental evaluations are conducted for a well-known test-case (i. e., pursuit game) in the context of two learning algorithms. The results show that the protocol is effective and the reinforcement learning algorithms using it perform much better.
収録刊行物
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- IEICE transactions on information and systems
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IEICE transactions on information and systems 88 (1), 127-134, 2005-01-01
一般社団法人電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1572543027349073664
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- NII論文ID
- 110003214145
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- NII書誌ID
- AA10826272
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- ISSN
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- CiNii Articles
