Deep neural networks are known to be vulnerable to adversarial attacks (AA).

Offline reinforcement learning (offline RL) [30,34]refers tothe setting where policies are trained using static, previously collected datasets. This presents an attractive paradigm for data reuse and safe policy learning in many applications, such as healthcare [62], autonomous driving [65], robotics [25, 48], and personalized recommendation systems [59].