Off-policy Evaluation in Doubly Inhomogeneous Environments

IF 3 1区 数学 Q1 STATISTICS & PROBABILITY Journal of the American Statistical Association Pub Date : 2024-09-09 DOI:10.1080/01621459.2024.2395593
Zeyu Bian, Chengchun Shi, Zhengling Qi, Lan Wang
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Abstract

This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions – temporal stationarity and individual homogeneity are both violated. To ha...
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双非均质环境中的非政策评估
这项工作旨在研究在违反两个关键强化学习(RL)假设--时间静止性和个体同质性--的情况下的非政策评价(OPE)。为了实现这一目标,我们需要...
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来源期刊
CiteScore
7.50
自引率
8.10%
发文量
168
审稿时长
12 months
期刊介绍: Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.
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