Genetic Takagi-Sugeno fuzzy reinforcement learning

被引：5

作者：

Yan, XW ^{[1
]}

Deng, ZD ^{[1
]}

Sun, ZQ ^{[1
]}

机构：

[1] Tsing Hua Univ, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China

来源：

PROCEEDINGS OF THE 2001 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL (ISIC'01) | 2001年

关键词：

reinforcement learning; genetic algorithms; Takagi-Sugeno fuzzy inference systems; neuro-fuzzy control;

D O I：

10.1109/ISIC.2001.971486

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents two fuzzy reinforcement learning methods for solving complicated learning tasks of continuous domains. Takagi-Sugeno fuzzy reinforcement learning (TSFRL) is constructed by combining Takagi-Sugeno type fuzzy inference systems with Q-learning. Next, genetic Takagi-Sugeno fuzzy reinforcement learning (GTSFRL) is introduced by embedding TSFRL into genetic algorithms. Both proposed learning algorithms can also be used to design Takagi-Sugeno fuzzy logic controllers. Experiments on the double inverted pendulum system demonstrate the performance and applicability of the proposed schemes. Finally, the conclusion remark is drawn.

引用

页码：67 / 72

页数：6

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