Genetic Network Programming with reinforcement learning for generating agent behavior in the benchmark problems

Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

研究成果: Paper査読

抄録

A new graph-based evolutionary algorithm named "Genetic Network Programming, GNP" has been proposed. GNP represents its solutions as graph structures which have distinguished expression ability. In this paper, we propose GNP with Reinforcement Learning. Evolutionary algorithm of GNP makes a very compact graph structure and Reinforcement Learning of GNP improves search speed for solutions.

本文言語English
ページ605-610
ページ数6
出版ステータスPublished - 2004 12 1
イベントSICE Annual Conference 2004 - Sapporo, Japan
継続期間: 2004 8 42004 8 6

Conference

ConferenceSICE Annual Conference 2004
CountryJapan
CitySapporo
Period04/8/404/8/6

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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