1. Adversarial examples construction towards white-box Q table variation in DQN pathfinding training;Bai,2018
2. Adversarial exploitation of policy imitation;Behzadan,2019
3. Vulnerability of deep reinforcement learning to policy induction attacks;Behzadan,2017
4. Towards evaluating the robustness of neural networks;Carlini,2017
5. Tutorial: towards robust deep learning against poisoning attacks;Chen;ACM Trans. Embed. Comput. Syst.,2022