An extension of Genetic Network Programming with Reinforcement Learning using actor-critic

被引：0

作者：

Hatakeyama, Hiroyuki ^{[1
]}

Mabu, Shingo ^{[1
]}

Hirasawa, Kotaro ^{[1
]}

Hu, Jinglu ^{[1
]}

机构：

[1] Waseda Univ, Grad Sch Informat Prod & Syst, Wakamatsu Ku, Hibikino 2-7, Kitakyushu, Fukuoka 8080135, Japan

来源：

2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6 | 2006年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1522 / +

页数：2

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