Policy gradient method using fuzzy controller in policies and its application

N. H. Noor Imanina; Harukazu Igarashi

Policy gradient method using fuzzy controller in policies and its application

N. H. Noor Imanina, Harukazu Igarashi

研究成果: Conference contribution

抄録

One of the reinforcement learning algorithms proposed by Igarashi and Ishihara is a combining method of policy gradient method and fuzzy control. In 2012, M. Sugimoto implemented the algorithm to the RoboCup Small Size League action decision system. The system received 30 scenes, taken from RoboCup Japan Open 2012 Competition to be learned. The purpose of this paper is to present the detailed analysis on the fuzzy rules in the policies taken from the system in order to find out the cause of the failure in the learning of 5 of the scenes received. A method was proposed to determine the rules that caused error in the learning of 5 scenes by evaluating the degree of contribution and divergence of each rule.

本文言語	English
ホスト出版物のタイトル	International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Held at the 3rd World Congress on Computing and Information Technology, WCIT
出版社	Society of Digital Information and Wireless Communications (SDIWC)
ページ	167-174
ページ数	8
出版ステータス	Published - 2014
イベント	International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014 - Kuala Lumpur, Malaysia 継続期間: 2014 11月 17 → 2014 11月 19

Other

Other	International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014
国/地域	Malaysia
City	Kuala Lumpur
Period	14/11/17 → 14/11/19

ASJC Scopus subject areas

人工知能
コンピュータビジョンおよびパターン認識

他のファイルとリンク

引用スタイル

Noor Imanina, N. H., & Igarashi, H. (2014). Policy gradient method using fuzzy controller in policies and its application. In International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Held at the 3rd World Congress on Computing and Information Technology, WCIT (pp. 167-174). Society of Digital Information and Wireless Communications (SDIWC).

Policy gradient method using fuzzy controller in policies and its application. / Noor Imanina, N. H.; Igarashi, Harukazu.

International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Held at the 3rd World Congress on Computing and Information Technology, WCIT. Society of Digital Information and Wireless Communications (SDIWC), 2014. p. 167-174.

研究成果: Conference contribution

Noor Imanina, NH & Igarashi, H 2014, Policy gradient method using fuzzy controller in policies and its application. in International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Held at the 3rd World Congress on Computing and Information Technology, WCIT. Society of Digital Information and Wireless Communications (SDIWC), pp. 167-174, International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Kuala Lumpur, Malaysia, 14/11/17.

Noor Imanina, N. H. ; Igarashi, Harukazu. / Policy gradient method using fuzzy controller in policies and its application. International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Held at the 3rd World Congress on Computing and Information Technology, WCIT. Society of Digital Information and Wireless Communications (SDIWC), 2014. pp. 167-174

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abstract = "One of the reinforcement learning algorithms proposed by Igarashi and Ishihara is a combining method of policy gradient method and fuzzy control. In 2012, M. Sugimoto implemented the algorithm to the RoboCup Small Size League action decision system. The system received 30 scenes, taken from RoboCup Japan Open 2012 Competition to be learned. The purpose of this paper is to present the detailed analysis on the fuzzy rules in the policies taken from the system in order to find out the cause of the failure in the learning of 5 of the scenes received. A method was proposed to determine the rules that caused error in the learning of 5 scenes by evaluating the degree of contribution and divergence of each rule.",

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AB - One of the reinforcement learning algorithms proposed by Igarashi and Ishihara is a combining method of policy gradient method and fuzzy control. In 2012, M. Sugimoto implemented the algorithm to the RoboCup Small Size League action decision system. The system received 30 scenes, taken from RoboCup Japan Open 2012 Competition to be learned. The purpose of this paper is to present the detailed analysis on the fuzzy rules in the policies taken from the system in order to find out the cause of the failure in the learning of 5 of the scenes received. A method was proposed to determine the rules that caused error in the learning of 5 scenes by evaluating the degree of contribution and divergence of each rule.

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Policy gradient method using fuzzy controller in policies and its application

抄録

Other

ASJC Scopus subject areas

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