Yu-Lin Jin

SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning

SEBA is a sample-efficient framework for black-box adversarial attacks on visual RL agents that significantly reduces cumulative rewards, preserves visual fidelity, and greatly decreases environment interactions compared to prior black-box and white-box methods.