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ID 33061
フルテキストURL
著者
Ito, Kazuyuki Okayama University
Gofuku, Akio Okayama University Kaken ID publons researchmap
抄録

Reinforcement learning is an adaptive and flexible control method for autonomous system. In our previous works, we had proposed a reinforcement learning algorithm for redundant systems: "Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)", and applied it to multi-agent systems. However previous works of the QDSEGA have been restricted to homogeneous agents. In this paper, we extend our previous works of multi-agent systems, and propose a hybrid autonomous control method for heterogeneous multi-agent systems. To demonstrate the effectiveness of the proposed method, simulations of transportation task by 10 heterogeneous mobile robots have been carried out. As a result effective behaviors have been obtained.

キーワード
adaptive control
learning (artificial intelligence)
mobile robots
multi-agent systems
備考
Digital Object Identifier: 10.1109/IROS.2003.1249245
Published with permission from the copyright holder.this is the institute's copy, as published in Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on, 27-31 Oct. 2003, Volume 3, Pages 2500-2505.
Publisher URL:http://dx.doi.org/10.1109/IROS.2003.1249245
Copyright © 2003 IEEE. All rights reserved.
発行日
2003-10
出版物タイトル
Intelligent Robots and Systems
3巻
開始ページ
2500
終了ページ
2505
資料タイプ
学術雑誌論文
言語
English
査読
有り
DOI
Submission Path
mechanical_engineering/3