Model-based reinforcement learning with model error and its application

被引：0

作者：

Tajima, Yoshiyuki ^{[1
]}

Onisawa, Takehisa ^{[1
]}

机构：

[1] Univ Tsukuba, Tsukuba, Ibaraki, Japan

来源：

PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-8 | 2007年

关键词：

reinforcement learning; model-based reinforcement learning; agent; robot;

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 proposes a Reinforcement Learning (RL) algorithm called Model Error based Forward Planning Reinforcement Learning (ME-FPRL). In this algorithm, an agent controls the amount of learning by using the model error. This study applies ME-FPRL to the pursuit of a target by a robot camera. The results of this application show that ME-FPRL is more efficient than Usual RL and Model-based RL.

引用

页码：1333 / 1336

页数：4

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