Presentation 2007-01-25
Adaptation of a Reinforcement Learning System IP MBN Using a Clustering Algorithm to Environmental Changes
Daisuke KITAKOSHI, Terumasa YAMAGUCHI, Hiroyuki SHIOYA, Ryohei NAKANO,
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Abstract(in English) We have proposed an adaptive learning system IPMBN using a mixture of Bayesian networks for agents, and also have investigated its adjustability to dynamic environments. Each component of the mixture (i.e. a Bayesian network) can be regarded as a stochastic knowledge representation corresponding to a policy in environment. The performance to represent the environmental information is considered to increase with the number of BNs incorporated into the mixture; however, greater computational cost is required to constitute the mixture. It is thus disirable that our system can render the environmental information with as less BNs as possible. In this paper, we introduce a clustering algorithm into IPMBN to extract BNs, which are applicable for representing a variety of policies, out of a quantity of BNs, and then discuss the performance of the system.
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Keyword(in English) Mixture of Bayesian Networks / Profit Sharing / Hellinger Distance / Clustering
Paper # NC2006-99
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Committee NC
Conference Date 2007/1/18(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Adaptation of a Reinforcement Learning System IP MBN Using a Clustering Algorithm to Environmental Changes
Sub Title (in English)
Keyword(1) Mixture of Bayesian Networks
Keyword(2) Profit Sharing
Keyword(3) Hellinger Distance
Keyword(4) Clustering
1st Author's Name Daisuke KITAKOSHI
1st Author's Affiliation Graduate School of Engineering, Nagoya Institute of Technology()
2nd Author's Name Terumasa YAMAGUCHI
2nd Author's Affiliation Faculty of Engineering, Muroran Institute of Technology
3rd Author's Name Hiroyuki SHIOYA
3rd Author's Affiliation Faculty of Engineering, Muroran Institute of Technology
4th Author's Name Ryohei NAKANO
4th Author's Affiliation Graduate School of Engineering, Nagoya Institute of Technology
Date 2007-01-25
Paper # NC2006-99
Volume (vol) vol.106
Number (no) 500
Page pp.pp.-
#Pages 6
Date of Issue