Robust Almost-Sure Reachability in Multi-Environment MDPs
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Abstract (arXiv)
Multiple-environment MDPs (MEMDPs) capture finite sets of MDPs that share the states but differ in the transition dynamics. These models form a proper subclass of partially observable MDPs (POMDPs). We consider the synthesis of policies that robustly satisfy an almost-sure reachability property in MEMDPs, that is, one policy that satisfies a property for all environments. For POMDPs, deciding the existence of robust policies is an EXPTIME-complete problem. In this paper, we show that this problem is PSPACE-complete for MEMDPs, while the policies in general require exponential memory. We exploit the theoretical results to develop and implement an algorithm that shows promising results in synthesizing robust policies for various benchmarks.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jan 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024Robust Markov Decision Processes: A Place Where AI and Formal Methods Meetcited by 18
- 2025Multi-Environment POMDPs: Discrete Model Uncertainty Under Partial Observabilitycited by 2
- 2025Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checkingcited by 2
- 2026Multi-Environment POMDPs with Finite-Horizon Objectivescited by 0
- 2026Multi-Environment POMDPs with Finite-Horizon Objectives8 May 2026
- 2025Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checking11 Nov 2025
- 2025Multi-Environment POMDPs: Discrete Model Uncertainty Under Partial Observability27 Oct 2025
- 2024Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet18 Nov 2024