An extension of genetic network programming with reinforcement learning using actor-critic

Hiroyuki Hatakeyama*, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

A new graph-based evolutionary algorithm named "Genetic Network Programming, GNP" has been already proposed. GNP represents its solutions as graph structures, which can improve the expression ability and performance. In addition, GNP with Reinforcement Learning (GNP-RL) was proposed a few years ago. Since GNP-RL can do reinforcement learning during task execution in addition to evolution after task execution, it can search for solutions efficiently. In this paper, GNP with Actor-Critic (GNP-AC) which is a new type of GNP-RL is proposed. Originally, GNP deals with discrete information, but GNP-AC aims to deal with continuous information. The proposed method is applied to the controller of the Khepera simulator and its performance is evaluated.

本文言語English
ホスト出版物のタイトル2006 IEEE Congress on Evolutionary Computation, CEC 2006
ページ1537-1543
ページ数7
出版ステータスPublished - 2006 12月 1
イベント2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
継続期間: 2006 7月 162006 7月 21

出版物シリーズ

名前2006 IEEE Congress on Evolutionary Computation, CEC 2006

Conference

Conference2006 IEEE Congress on Evolutionary Computation, CEC 2006
国/地域Canada
CityVancouver, BC
Period06/7/1606/7/21

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア
  • 理論的コンピュータサイエンス

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