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.
| Original language | English |
|---|---|
| Pages | 2500-2505 |
| Number of pages | 6 |
| Publication status | Published - Dec 26 2003 |
| Event | 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems - Las Vegas, NV, United States Duration: Oct 27 2003 → Oct 31 2003 |
Other
| Other | 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems |
|---|---|
| Country | United States |
| City | Las Vegas, NV |
| Period | 10/27/03 → 10/31/03 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications
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Cite this
Ito, K., & Gofuku, A. (2003). Hybrid autonomous control for heterogeneous multi-agent system. 2500-2505. Paper presented at 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, United States.