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math.OC2025
Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Shengbo Wang, Nian Si
Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finit…
cs.LG2025
Near-Optimal Sample Complexities of Divergence-based S-rectangular Distributionally Robust Reinforcement Learning
Zhenghao Li, Shengbo Wang, Nian Si
Distributionally robust reinforcement learning (DR-RL) has recently gained significant attention as a principled approach that addresses discrepancies between training and testing…
cs.LG2025
Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning
Zijun Chen, Shengbo Wang, Nian Si
Motivated by practical applications where stable long-term performance is critical-such as robotics, operations research, and healthcare-we study the problem of distributionally ro…