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

Hiroyuki Hatakeyama, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages1537-1543
Number of pages7
Publication statusPublished - 2006 Dec 1
Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Duration: 2006 Jul 162006 Jul 21

Publication series

Name2006 IEEE Congress on Evolutionary Computation, CEC 2006

Conference

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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    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 (pp. 1537-1543). [1688491] (2006 IEEE Congress on Evolutionary Computation, CEC 2006).