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20242026
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cs.AI2026

Quantitative Analysis of -Regular Robust MDPs

Ali Asadi, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady +3

Robust Markov Decision Processes (RMDPs) generalize classical MDPs by allowing uncertainty in transition probabilities and optimizing against their worst-case realization. We consi…

cs.AI2026

Strongly Polynomial Time Complexity of Policy Iteration for Robust MDPs

Ali Asadi, Krishnendu Chatterjee, Ehsan Goharshady +3

Markov decision processes (MDPs) are a fundamental model in sequential decision making. Robust MDPs (RMDPs) extend this framework by allowing uncertainty in transition probabilitie…

cs.AI2026

Automated Approach for Solving Infinite-state Polynomial Reachability Games

Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi +2

Reachability games are two-player games played on a graph, where the objective of player is to reach the target set whereas the objective of player…

cs.AI2025

Qualitative Analysis of -Regular Objectives on Robust MDPs

Ali Asadi, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady +2

Robust Markov Decision Processes (RMDPs) generalize classical MDPs that consider uncertainties in transition probabilities by defining a set of possible transition functions. An ob…

cs.AI2024

Solving Long-run Average Reward Robust MDPs via Stochastic Games

Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi +2

Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities in…