| ID | 33061 |
| FullText URL | |
| Author |
Ito, Kazuyuki
|
| 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 |
English
|
| Refereed |
True
|
| DOI | |
| Submission Path | mechanical_engineering/3
|
