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This paper studies robust peak-cost constrained reinforcement learning, addressing limitations of standard CMDPs by controlling the maximum cost along a trajectory and considering dynamics uncertainty. The authors show zero duality gap may not hold and propose a surrogate optimization framework with robust value estimation.