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Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learning
Reinforcement learning (RL) policies trained in simulation often degrade once deployed on real systems, where the controller must reject external disturbances that were never encountered in simulation. Robust RL addresses this by exposing the controller to perturbations while it learns, through doma
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