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

Hiroyuki Hatakeyama; Shingo Mabu; Kotaro Hirasawa; Jinglu Hu

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

Hiroyuki Hatakeyama, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages	1537-1543
Number of pages	7
Publication status	Published - 2006 Dec 1
Event	2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada Duration: 2006 Jul 16 → 2006 Jul 21

Publication series

Name	2006 IEEE Congress on Evolutionary Computation, CEC 2006

Conference

Conference	2006 IEEE Congress on Evolutionary Computation, CEC 2006
Country	Canada
City	Vancouver, BC
Period	06/7/16 → 06/7/21

ASJC Scopus subject areas

Artificial Intelligence
Software
Theoretical Computer Science

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An extension of genetic network programming with reinforcement learning using actor-critic. / Hatakeyama, Hiroyuki; Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu.

2006 IEEE Congress on Evolutionary Computation, CEC 2006. 2006. p. 1537-1543 1688491 (2006 IEEE Congress on Evolutionary Computation, CEC 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hatakeyama, H, Mabu, S, Hirasawa, K & Hu, J 2006, An extension of genetic network programming with reinforcement learning using actor-critic. in 2006 IEEE Congress on Evolutionary Computation, CEC 2006., 1688491, 2006 IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1537-1543, 2006 IEEE Congress on Evolutionary Computation, CEC 2006, Vancouver, BC, Canada, 06/7/16.

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AB - 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.

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

Abstract

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Conference

ASJC Scopus subject areas

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