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Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies

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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.

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