Hongbin Liang — accepted AI-conference papers

2025

Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective

Tianyang Duan, Zongyuan Zhang, Zheng Lin, Yue Gao, Ling Xiong, Yong Cui +4

ICASSP 2025accepted

Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in real-world applications. Adversarial attack is an effective method for evaluating the robustness of DRL agents. However, existing attack methods targeting individual sampled actions have limite…

Cited by 16 Source ↗Scholar ↗

2025

Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks

Zongyuan Zhang, Tianyang Duan, Zheng Lin, Dong Huang, Zihan Fang, Zekai Sun +5

IROS 2025

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 t

Cited by 12 Source ↗Scholar ↗