Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning

Zexin Li; Ruili Yao; Yiming Zeng; Xiaoxue Gao

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arXiv · 2610.06083

Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning

Zexin Li·Ruili Yao·Yiming Zeng·Xiaoxue Gao

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Abstract

Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This paper studies transfer-based black-box attacks on DRL: the attacker crafts observation perturbations on a white-box surrogate agent and feeds them to an unknown victim. We formulate the attack as return minimization under a per-step perturbation budget. We first show that transplanting transferable image-classification attacks (FGSM, MI-FGSM, and NI-FGSM) with a per-step objective yields perturbations that transfer but are no stronger than random noise of the same budget. We then propose a trajectory-level attack that optimizes a sequence of perturbations over a receding horizon through a differentiable model of the environment and a temperature-smoothed surrogate policy, with the same optimizers. On CartPole-v1 with ten DQN and DDQN agents and 100 surrogate--victim pairs, the trajectory-level attack outperforms per-step attacks and random noise in the white-box, cross-model, and cross-algorithm settings.

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BibTeX RIS

Zexin Li, Ruili Yao, Yiming Zeng, Xiaoxue Gao. 2026-10-05. Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning. https://arxiv.org/abs/2610.06083

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