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Evidence record 16089 · automatically gathered

Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies

Sequential decision-making in real-world applications often involves uncertainty about the environment's model. Uncertain Markov decision processes (UMDPs) represent the possible environments as a set of MDPs with shared states and actions but potentially different transition probabilities and rewards. Optimizing a single policy across all possible MDPs may sacrifice performance, while preparing an individually optimized policy for every MDP may violate operational, regulatory, or interpretabili

Record details

Published: 3 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Environment
Retrieved: 4 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (3 August 2026), “Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies,” evidence record 16089, https://ethics.ai/record/16089 (originally published by arXiv cs.AI).

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