Reinforcement learning in the fuzzy classifier system

被引：11

作者：

Valenzuela-Rendon, M ^{[1
]}

机构：

[1] Inst Tecnol & Estudios Super Monterrey, Ctr Inteligencia Artificial, Monterrey 64869, NL, Mexico

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 1998年 / 14卷 / 1-2期

关键词：

D O I：

10.1016/S0957-4174(97)00077-8

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 describes the fuzzy classifier system and a new payoff distribution scheme that performs true reinforcement learning. The fuzzy classifier system is a crossover between learning classifier systems and fuzzy logic controllers. By the use of fuzzy logic, the fuzzy classifier system allows for variables to take continuous values, and thus, could be applied to the identification and control of continuous dynamic systems. The fuzzy classifier system adapt the mechanics of learning classifier system to fuzzy logic to evolve sets of coadapted fuzzy rules. The payoff distribution scheme presented here opens the way for the use of the fuzzy classifier system in control tasks. Additionally, other mechanisms that improve learning speed are presented. (C) 1998 Elsevier Science Ltd. All rights reserved.

引用

页码：237 / 247

页数：11

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