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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Held at the 3rd World Congress on Computing and Information Technology, WCIT
Publisher	Society of Digital Information and Wireless Communications (SDIWC)
Pages	167-174
Number of pages	8
Publication status	Published - 2014
Event	International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014 - Kuala Lumpur, Malaysia Duration: 2014 Nov 17 → 2014 Nov 19

Other

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

Keywords

Action decision system
Fuzzy control
Fuzzy rules
Policy gradient method
Reinforcement learning
RoboCup Small Size League

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition

Cite this

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.

Research output: Chapter in Book/Report/Conference proceeding › 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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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

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Keywords

ASJC Scopus subject areas

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