Hybrid autonomous control for heterogeneous multi-agent system

Kazuyuki Ito, Akio Gofuku

Research output: Contribution to conferencePaper

3 Citations (Scopus)

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 languageEnglish
Pages2500-2505
Number of pages6
Publication statusPublished - Dec 26 2003
Event2003 IEEE/RSJ International Conference on Intelligent Robots and Systems - Las Vegas, NV, United States
Duration: Oct 27 2003Oct 31 2003

Other

Other2003 IEEE/RSJ International Conference on Intelligent Robots and Systems
CountryUnited States
CityLas Vegas, NV
Period10/27/0310/31/03

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.