Presentation 2008-03-28
A Multi-agent Reinforcement Learning with Parallel Computation and its Application to Chaos Control
Norihisa SATO, Masaharu ADACHI,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) A reinforcement learning is a trial and error process, therefore it takes a huge computation time. It multiple agents can learn simultaneously the computational time can be reduced. In this paper we attempt to apply a multi-agent reinforcement learning to chaos control. We implement a multi-agent reinforcement learning using Message-Passing Interface. We compared the control performance of the multi-agent reinforcement learning and that of a single agent reinforcement learning. As a result, the multi-agent reinforcement learning show almost the same performance with the single-agent one. Even if learning iterate for a many times. Moreover, it is suggested that the quantized states whose action after learning by the multi-agent are the same, are important locations for the chaos control.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) control of chaos / reinforcement lerning / MPI (Message-Passing Interface)
Paper # NLP2007-169
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Conference Information
Committee NLP
Conference Date 2008/3/21(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Multi-agent Reinforcement Learning with Parallel Computation and its Application to Chaos Control
Sub Title (in English)
Keyword(1) control of chaos
Keyword(2) reinforcement lerning
Keyword(3) MPI (Message-Passing Interface)
1st Author's Name Norihisa SATO
1st Author's Affiliation School of Engineering, Tokyo Denki University()
2nd Author's Name Masaharu ADACHI
2nd Author's Affiliation School of Engineering, Tokyo Denki University
Date 2008-03-28
Paper # NLP2007-169
Volume (vol) vol.107
Number (no) 561
Page pp.pp.-
#Pages 6
Date of Issue