Rectangularity and duality of distributionally robust Markov Decision Processes
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Abstract (arXiv)
The main goal of this paper is to discuss several approaches to 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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Aug 2023.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 2.
6 papers.
- 2023On the Foundation of Distributionally Robust Reinforcement Learningcited by 18
- 2023Distributionally Robust Model-based Reinforcement Learning with Large State Spacescited by 17
- 2025Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Dualitycited by 4
- 2024Robust Reinforcement Learning with Dynamic Distortion Risk Measurescited by 3
- 2024Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spacescited by 0
- 2025Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Setscited by 0
- 2025Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets22 Oct 2025
- 2025Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality24 Aug 2025
- 2024Robust Reinforcement Learning with Dynamic Distortion Risk Measures16 Sep 2024
- 2024Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces17 Jun 2024
- 2023On the Foundation of Distributionally Robust Reinforcement Learning15 Nov 2023
- 2023Distributionally Robust Model-based Reinforcement Learning with Large State Spaces5 Sep 2023