Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learning.

arXiv:2603.12110v2 Announce Type: replace-cross Abstract: 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.