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

arXiv:2511.09681 · cs.LG, cs.AI · Submitted 2025-11-12 · Read on arXiv

cs.LG, cs.AI

Submitted: 2025-11-12

Updated: 2026-09-02

Comments: Accepted to CVPR 2026

Journal ref: Huang T, Jin Y, Liu J, et al. SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2026: 27861-27871

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 35× in this paper · explained in 4 episodes
• World Model — 25× in this paper · explained in 7 episodes
• Adversarial Attacks — 9× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 9× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Action Space — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Sample Efficiency — 5× in this paper · explained in 4 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Actor — 3× in this paper · explained in 3 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Embodied AI — 2× in this paper · explained in 2 episodes
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Model Drift — 1× in this paper · explained in 4 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Two-Stage Framework — 1× in this paper · explained in 2 episodes

Sources

• Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation

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