Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey

The survey presents a comprehensive analysis of adversarial training methodologies aimed at enhancing the robustness of Deep Reinforcement Learning (DRL) agents against environmental perturbations. It systematically categorizes various adversarial attacks and training techniques, comparing their objectives and mechanisms. This work is significant for practitioners as it provides insights into improving the reliability of DRL systems in real-world applications, addressing a critical challenge in deploying these models.