Fuzzy adaptive Q-learning method with dynamic learning parameters

被引：0

作者：

Maeda, Y ^{[1
]}

机构：

[1] Osaka Electrocommun Univ, Fac Informat Sci & Technol, Neyagawa, Osaka 5728530, Japan

来源：

JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5 | 2001年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An active search in the reinforcement learning disturbs the learning process when learning proceeds and converges to a partial search area. Therefore, it is important to balance between searching behavior of the unknown knowledge and using behavior of the obtained knowledge. In this research, we propose an adaptive Q-learning method tuning learning parameters of the reinforcement learning by fuzzy rules. We also report some results of artificial ants simulation using this method.

引用

页码：2778 / 2780

页数：3