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The paper introduces Adaptive Gradient-Masked Reinforcement (AGMR) Attack, a novel white-box adversarial attack method designed to effectively target deep reinforcement learning (DRL) agents in robotic control by addressing the limitations of existing supervised learning-based attacks. AGMR uses a gradient-based soft masking mechanism to dynamically identify and selectively perturb critical state dimensions, optimizing the adversarial policy for maximum impact on long-term rewards. Experiments demonstrate that AGMR significantly outperforms existing adversarial attack methods in reducing victim agent performance and improving robustness through adversarial training.
By selectively targeting the most influential state features, AGMR achieves significantly stronger adversarial attacks against DRL-controlled robots than prior methods that indiscriminately perturb all state dimensions.
Deep reinforcement learning (DRL) has emerged as a promising approach for robotic control, but its real-world deployment remains challenging due to its vulnerability to environmental perturbations. Existing white-box adversarial attack methods, adapted from supervised learning, fail to effectively target DRL agents as they overlook temporal dynamics and indiscriminately perturb all state dimensions, limiting their impact on long-term rewards. To address these challenges, we propose the Adaptive Gradient-Masked Reinforcement (AGMR) Attack, a white-box attack method that combines DRL with a gradient-based soft masking mechanism to dynamically identify critical state dimensions and optimize adversarial policies. AGMR selectively allocates perturbations to the most impactful state features and incorporates a dynamic adjustment mechanism to balance exploration and exploitation during training. Extensive experiments demonstrate that AGMR outperforms state-of-the-art adversarial attack methods in degrading the performance of the victim agent and enhances the victim agent’s robustness through adversarial defense mechanisms.
Tsinghua AI