Genetic network programming with reinforcement learning and its application to making mobile robot behavior

Shingo Mabu, Hiroyuki Hatakeyamay, Moe Thu Thu, Kotaro Hirasawa, Jinglu Hu

研究成果: Article査読

19 被引用数 (Scopus)

抄録

A new graph-based evolutionary algorithm called "Genetic Network Programming, GNP" has been proposed. The solutions of GNP are represented as graph structures, which can improve the expression ability and performance. In addition, GNP with Reinforcement Learning (GNP-RL) has been proposed to search for solutions efficiently. GNP-RL can use current information (state and reward) and change its programs during task execution. Thus, it has an advantage over evolution-based algorithms in case much information can be obtained during task execution. The GNP we proposed in the previous research deals with discrete information, but in this paper, we extend the conventional GNP-RL which can deal with numerical information. The proposed method is applied to the controller of Khepera simulator and its performance is evaluated.

本文言語English
ページ(範囲)1009-1015
ページ数7
ジャーナルIEEJ Transactions on Electronics, Information and Systems
126
8
DOI
出版ステータスPublished - 2006

ASJC Scopus subject areas

  • 電子工学および電気工学

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