Papers › Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning

Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning

30 Jun 2021arXiv:2106.15860archive 2025-07-28

You Qiaoben, Chengyang Ying, Xinning Zhou, Hang Su, Jun Zhu, Bo Zhang

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease a victim's cumulative expected reward by manipulating the victim's observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not be able to achieve the lowest cumulative reward since they do not explore the environmental dynamics in general. In this paper, we provide a framework to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. Our reformulation generates an optimal adversary in the function space of the targeted attacks, repelling them via a generic two-stage framework. In the first stage, we train a deceptive policy by hacking the environment, and discover a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, we theoretically show that our adversary is stronger under an appropriate noise level. Extensive experiments demonstrate our method's superiority in terms of efficiency and effectiveness, achieving the state-of-the-art performance in both Atari and MuJoCo environments.

Paper PDF Code

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

yingchengyang/Two-Stage-Attack officialmentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Deep Reinforcement Learning MuJoCo Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections