Reinforcement learning-based tuning algorithm applied to fuzzy identification

被引：0

作者：

Cerrada, Maxiela ^{[1
]}

Aguilar, Jose

Titli, Andre

机构：

[1] Univ Los Andes, Control Syst Dept, CEMISID, Merida, Venezuela

[2] CNRS, LAAS, DISCO Grp, Toulouse 4, France

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1 | 2006年 / 3971卷

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：

In on-line applications, reinforcement learning based algorithms allow to take into account the environment information in order to propose an action policy for the overall optimization objectives. In this work, it is presented a learning algorithm based oil reinforcement learning and temporal differences allowing the on-line parameters adjustment for identification tasks. As a consequence, the reinforcement signal is generically defined in order to minimize the temporal difference.

引用

页码：623 / 630

页数：8

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