Policy gradient approach for learning of soccer player agents: Pass selection of midfielders

Harukazu Igarashi, Hitoshi Fukuoka, Seiji Ishihara

研究成果: Conference contribution

抄録

This research develops a learning method for the pass selection problem of midfielders in RoboCup Soccer Simulation games. A policy gradient method is applied as a learning method to solve this problem because it can easily represent the various heuristics of pass selection in a policy function. We implement the learning function in the midfielders' programs of a well-known team, UvA Trilearn Base 2003. Experimental results show that our method effectively achieves clever pass selection by midfielders in full games. Moreover, in this method's framework, dribbling is learned as a pass technique, in essence to and from the passer itself. It is also shown that the improvement in pass selection by our learning helps to make a team much stronger.

本文言語English
ホスト出版物のタイトルIntelligent Control and Computer Engineering
ページ137-148
ページ数12
DOI
出版ステータスPublished - 2011 1 10
イベントInternational Conference on Advances in Intelligent Control and Computer Engineering - Hong Kong, Hong Kong
継続期間: 2010 3 172010 3 19

出版物シリーズ

名前Lecture Notes in Electrical Engineering
70 LNEE
ISSN(印刷版)1876-1100
ISSN(電子版)1876-1119

Conference

ConferenceInternational Conference on Advances in Intelligent Control and Computer Engineering
CountryHong Kong
CityHong Kong
Period10/3/1710/3/19

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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