start-ver=1.4
cd-journal=joma
no-vol=3
cd-vols=
no-issue=
article-no=
start-page=2500
end-page=2505
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2003
dt-pub=200310
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Hybrid autonomous control for heterogeneous multi-agent system
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=<p>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: &quot;Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)&quot;, 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. </p>

en-copyright=
kn-copyright=

en-aut-name=ItoKazuyuki
en-aut-sei=Ito
en-aut-mei=Kazuyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=GofukuAkio
en-aut-sei=Gofuku
en-aut-mei=Akio
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

affil-num=1
en-affil=
kn-affil=Okayama University

affil-num=2
en-affil=
kn-affil=Okayama University
en-keyword=adaptive control
kn-keyword=adaptive control
en-keyword=   learning (artificial intelligence)
kn-keyword=   learning (artificial intelligence)
en-keyword=mobile robots
kn-keyword=mobile robots
en-keyword=multi-agent systems
kn-keyword=multi-agent systems
END