arXiv AI September 2, 2026 By Tairan Huang, Yulin Jin, Junxu Liu, Qingqing Ye, Haibo Hu

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

SEBA is a sample‑efficient framework for black‑box adversarial attacks on visual reinforcement learning agents. It combines a shadow Q model, a generative adversarial network for imperceptible perturbations, and a world model to simulate dynamics, reducing real‑world queries. Experiments on MuJoCo and Atari show SEBA significantly lowers cumulative rewards while preserving visual fidelity and requiring far fewer environment interactions than previous methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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By Chenyang Li, Kaige Li, Zeyu Jiang, Changhao Chen

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