arXiv AI
Solving Robust POMDPs with Omega-regular Objectives via Partially Observable Stochastic Games
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Robust Markov Decision Processes (RMDPs) generalize classical MDPs by allowing uncertainty in transition probabilities and optimizing against their worst-case realization. We consider $(s,a)$-rectangu...
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Strongly Polynomial Time Complexity of Policy Iteration for $L_\infty$ Robust MDPs
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Linear Programming Representations and Strongly Polynomial Algorithms for Robust Markov Decision Processes
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Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees
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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.