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 172014 11 19

Other

OtherInternational Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014
CountryMalaysia
CityKuala Lumpur
Period14/11/1714/11/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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