Diffusion Guided Adversarial State Perturbations in Reinforcement Learning

The recent publication on SHIFT reveals critical vulnerabilities in reinforcement learning (RL) systems, particularly in vision-based environments where adversarial attacks can mislead agents through subtle image manipulations. While current defenses have shown some robustness, they are fundamentally limited by the weaknesses of existing lp norm-constrained attacks, which do not sufficiently alter the semantics of inputs. SHIFT proposes a novel approach that generates semantically different yet realistic perturbed states, effectively breaking through existing defenses. This advancement underscores the importance of developing robust policies to protect RL systems from increasingly sophisticated adversarial threats, as highlighted by the study's evaluations showing SHIFT's superior performance over traditional methods.