A repeated retraining algorithm with robust gradient estimation converges to an approximately stable policy in performative RL under Huber contamination, with approximation error scaling as the square root of the corruption level.
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On Corruption-Robustness in Performative Reinforcement Learning
A repeated retraining algorithm with robust gradient estimation converges to an approximately stable policy in performative RL under Huber contamination, with approximation error scaling as the square root of the corruption level.