Genetic Network Programming with Actor-Critic | Zendy

Hiroyuki Hatakeyama | Zendy; Shingo Mabu | Zendy; Kotaro Hirasawa | Zendy; Jinglu Hu | Zendy

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Genetic Network Programming with Actor-Critic

Author(s) -

Hiroyuki Hatakeyama,

Shingo Mabu,

Kotaro Hirasawa,

Jinglu Hu

Publication year - 2007

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2007.p0079

Subject(s) - computer science , reinforcement learning , genetic programming , graph , task (project management) , artificial intelligence , genetic algorithm , controller (irrigation) , machine learning , theoretical computer science , management , agronomy , economics , biology

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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