Two-Stage Reinforcement Learning on Credit Branch Genetic Network Programming for Mobile Robots
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- Sendari Siti
- The State University of Malang The Graduate School of Information, Production, and Systems, Waseda University
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- Mabu Shingo
- The Graduate School of Information, Production, and Systems, Waseda University
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- Hirasawa Kotaro
- The Graduate School of Information, Production, and Systems, Waseda University
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This paper proposes Two-Stage Reinforcement Learning on Credit Branch Genetic Network Programming named GNP-TSRL-CB for mobile robots. The proposed method uses 2 kinds of Q-tables for sub node selection and credit branch selection, which has advantages of (1) determining an alternative function by using sub node selection and (2) skipping useless functions by using credit branch selection. It is clarified from simulation results that the adaptability mechanism of the proposed method can improve the performance compared with the conventional methods when the individuals of GNP-TSRL-CB are implemented in the dynamic environments like the sudden changes occur.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 133 (4), 856-863, 2013
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679586247680
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- NII論文ID
- 10031161285
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 024642887
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可