ArticleAnnals of statistics2022
BATCH POLICY LEARNING IN AVERAGE REWARD MARKOV DECISION PROCESSES.
Article in Annals of statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- REINFORCEMENT LEARNING FOR INDIVIDUAL OPTIMAL POLICY FROM HETEROGENEOUS DATA.Annals of statistics · 2025Article
- MULTIVARIATE DYNAMIC MEDIATION ANALYSIS UNDER A REINFORCEMENT LEARNING FRAMEWORK.Annals of statistics · 2025Article
- Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.Journal of machine learning research : JMLR · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
We consider the batch (off-line) policy learning problem in the infinite horizon Markov Decision Process. Motivated by mobile health applications, we focus on learning a policy that maximizes the long-term average reward. We propose a doubly robust estimator for the average reward and show that it achieves semiparametric efficiency. Further we develop an optimization algorithm to compute the optimal policy in a parameterized stochastic policy class. The performance of the estimated policy is measured by the difference between the optimal average reward in the policy class and the average reward of the estimated policy and we establish a finite-sample regret guarantee. The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
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