Global Policy Construction in Modular Reinforcement Learning

被引：0

作者：

Zhang, Ruohan ^{[1
]}

Song, Zhao ^{[1
]}

Ballard, Dana H. ^{[1
]}

机构：

[1] Univ Texas Austin, Dept Comp Sci, 2317 Speedway,Stop D9500, Austin, TX 78712 USA

来源：

PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2015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：

We propose a modular reinforcement learning algorithm which decomposes a Markov decision process into independent modules. Each module is trained using Sarsa(lambda). We introduce three algorithms for forming global policy from modules policies, and demonstrate our results using a 2D grid world.

引用

页码：4226 / 4227

页数：2

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