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 |
|---|---|
| Title of host publication | IEEE International Conference on Intelligent Robots and Systems |
| Pages | 2500-2505 |
| Number of pages | 6 |
| Volume | 3 |
| Publication status | Published - 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 |
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ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
Hybrid autonomous control for heterogeneous multi-agent system. / Ito, Kazuyuki; Gofuku, Akio.
IEEE International Conference on Intelligent Robots and Systems. Vol. 3 2003. p. 2500-2505.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hybrid autonomous control for heterogeneous multi-agent system
AU - Ito, Kazuyuki
AU - Gofuku, Akio
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0348040315&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0348040315&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0348040315
VL - 3
SP - 2500
EP - 2505
BT - IEEE International Conference on Intelligent Robots and Systems
ER -