start-ver=1.4
cd-journal=joma
no-vol=
cd-vols=
no-issue=
article-no=
start-page=175
end-page=180
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2004
dt-pub=20048
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=A study of reinforcement learning with knowledge sharing
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=<p>In this paper, we consider multi-agent system in which every agents have own tasks that differs each other. We propose a method that decreases learning time of reinforcement learning by using the model of environment. In the proposed algorithm, the model is created by sharing the experiences of agents each other. To demonstrate the effectiveness of the proposed method, simulations of a puddle world and experiments of a maze world have been carried out. As a result effective behaviors have been obtained quickly.</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=ImotoYoshiaki
en-aut-sei=Imoto
en-aut-mei=Yoshiaki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=TaguchiHideaki
en-aut-sei=Taguchi
en-aut-mei=Hideaki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
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=4
ORCID=

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

affil-num=2
en-affil=
kn-affil=Okayama University

affil-num=3
en-affil=
kn-affil=Okayama University

affil-num=4
en-affil=
kn-affil=Okayama University
END