Rectangularity and Duality of Distributionally Robust Markov Decision Processes

Abstract The main goal of this paper is to discuss several approaches to the formulation of distributionally robust counterparts of Markov Decision Processes, where the transition kernels are not specified exactly but rather are assumed to be elements of the corresponding ambiguity sets. The intent is to clarify some connections between the game and static formulations of distributionally robust MDPs, and delineate the role of rectangularity associated with ambiguity sets in determining these connections.

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Publication Details

Journal
Mathematical Programming
Published
2025-11-03
DOI
https://doi.org/10.1007/s10107-025-02297-y
Primary Topic
Risk and Portfolio Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Rectangularity and Duality of Distributionally Robust Markov Decision Processes

Alexander Shapiro, Yan Li
Mathematical Programming
Risk and Portfolio Optimization
article

Rectangularity and Duality of Distributionally Robust Markov Decision Processes

Alexander Shapiro, Yan Li
article en

Abstract

Abstract The main goal of this paper is to discuss several approaches to the formulation of distributionally robust counterparts of Markov Decision Processes, where the transition kernels are not specified exactly but rather are assumed to be elements of the corresponding ambiguity sets. The intent is to clarify some connections between the game and static formulations of distributionally robust MDPs, and delineate the role of rectangularity associated with ambiguity sets in determining these connections.

Mathematical Programming
Georgia Institute of Technology (US), Texas A&M University (US)
Air Force Office of Scientific Research
Openalex Percentile: Top 62%
Risk and Portfolio Optimization
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