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ID 33061
FullText URL
Author
Ito, Kazuyuki
Gofuku, Akio Kaken ID researchmap
Abstract

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.

Keywords
adaptive control
learning (artificial intelligence)
mobile robots
multi-agent systems
Note
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.
Published Date
2003-10
Publication Title
Intelligent Robots and Systems
Volume
volume3
Start Page
2500
End Page
2505
Content Type
Journal Article
language
英語
Refereed
True
DOI
Submission Path
mechanical_engineering/3