Post-Hoc Robustness for Model-Based Reinforcement Learning

ArXi:2606.03521v1 Announce Type: new To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations. In this setting, a protagonist agent optimizes a policy under environmental perturbations from an adversary, resulting in a zero-sum Marko game. When adversarially robust RL is combined with model-based RL, the adversary can target a learned transition model instead of the