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.
Authors
- Alexander Shapiro (ORCID: https://orcid.org/0000-0002-4776-0053)
- Yan Li (ORCID: https://orcid.org/0000-0002-6674-2483)
Institutions
- Georgia Institute of Technology (US)
- Texas A&M University (US)
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
Funders
- Air Force Office of Scientific Research