Linguistic reward-oriented Takagi-Sugeno fuzzy reinforcement learning

被引：0

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：

10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3: MEETING THE GRAND CHALLENGE: MACHINES THAT SERVE PEOPLE | 2001年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a new learning method to attack two significant sub-problems in reinforcement learning at the same time: continuous space and linguistic rewards. Linguistic reward-oriented Takagi-Sugeno fuzzy reinforcement learning (LRTSFRL) is constructed by combining Q-learning with Takagi-Sugeno type fuzzy inference systems. The proposed paradigm is capable of solving complicated learning tasks of continuous domains, also can be used to design Takagi-Sugeno fuzzy logic controllers. Experiments on the double inverted pendulum system demonstrate the performance and applicability of the presented scheme. Finally, the conclusion remark is drawn.

引用

页码：533 / 536

页数：4