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New SEBA framework enables sample-efficient black-box attacks on visual RL

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SEBA, a new framework designed to efficiently launch black-box adversarial attacks against visual reinforcement learning agents. This method integrates a shadow Q model to estimate rewards under adversarial conditions, a generative adversarial network for creating imperceptible perturbations, and a world model to minimize real-world queries. SEBA has demonstrated significant success in reducing cumulative rewards and maintaining visual fidelity across benchmarks like MuJoCo and Atari, while requiring fewer environment interactions than previous methods. AI

IMPACT This research could lead to more robust visual reinforcement learning agents by highlighting vulnerabilities to adversarial attacks.

RANK_REASON The cluster contains an academic paper detailing a new method for adversarial attacks on visual reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SEBA framework enables sample-efficient black-box attacks on visual RL

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The cluster contains an academic paper detailing a new method for adversarial attacks on visual reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tairan Huang, Yulin Jin, Junxu Liu, Qingqing Ye, Haibo Hu ·

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

    arXiv:2511.09681v2 Announce Type: replace-cross Abstract: Visual reinforcement learning has achieved remarkable progress in visual control and robotics, but its vulnerability to adversarial perturbations remains underexplored. Most existing black-box attacks focus on vector-based…

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