Optimizing Quantiles in Preference-Based Markov Decision Processes

被引：0

作者：

Gilbert, Hugo ^{[1
]}

Weng, Paul ^{[2
,3
,4
]}

Xu, Yan ^{[2
,3
,4
]}

机构：

[1] UPMC Univ Paris 06, Sorbonne Univ, CNRS, LIP6,UMR 7606, Paris, France

[2] SYSU CMU Joint Inst Engn, Guangzhou, Guangdong, Peoples R China

[3] Sch Elect & Informat Technol, Guangzhou, Guangdong, Peoples R China

[4] SYSU CMU Shunde Int Joint Res Inst, Shunde, Peoples R China

来源：

THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2017年

关键词：

MINIMIZING RISK MODELS; VARIANCE; UTILITY; POLICY;

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 the Markov decision process model, policies are usually evaluated by expected cumulative rewards. As this decision criterion is not always suitable, we propose in this paper an algorithm for computing a policy optimal for the quantile criterion. Both finite and infinite horizons are considered. Finally we experimentally evaluate our approach on random MDPs and on a data center control problem.

引用

页码：3569 / 3575

页数：7

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