KAPS: Exact Algorithm for Minimax Regret in Uncertain MDPs with Small Policy Sets
Researchers introduced k-adaptable policy synthesis, a new approach for optimizing a set of k policies under a minimax-regret objective in uncertain Markov decision processes (UMDPs). The problem is proven NP-hard, and the team developed KAPS, an exact nested branch-and-bound algorithm. This work addresses real-world sequential decision-making where model uncertainty is resolved shortly before execution, allowing selection of the most suitable policy from a limited prepared set. The research is available on arXiv with ID 2608.02509.
Key facts
- The paper is titled 'Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies'.
- It is available on arXiv with ID 2608.02509.
- The research introduces k-adaptable policy synthesis.
- The objective is to minimize maximum regret (minimax regret).
- The problem is proven to be NP-hard.
- The authors developed an exact algorithm called KAPS.
- KAPS uses a nested branch-and-bound approach.
- The setting involves model uncertainty resolved shortly before execution.
Entities
Institutions
- arXiv