Expert Selection in High-Dimensional Markov Decision Processes

被引：0

作者：

Rubies-Royo, Vicenc ^{[1
]}

Mazumdar, Eric ^{[1
]}

Dong, Roy ^{[1
]}

Tomlin, Claire ^{[1
]}

Sastry, S. Shankar ^{[1
]}

机构：

[1] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA

来源：

2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) | 2020年

关键词：

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：

In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings. Our method takes a set of candidate expert policies and switches between them to rapidly identify the best performing expert using a variant of the classical upper confidence bound algorithm, thus ensuring low regret in the overall performance of the system. This is useful in applications where several expert policies may be available, and one needs to be selected at run-time for the underlying environment.

引用

页码：3604 / 3610

页数：7

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