Robust Deep Reinforcement Learning Through Adversarial Attacks and
  Training : A Survey
Lucas Schott Josephine Delas Hatem Hajri Elies Gherbi Reda Yaich Nora Boulahia-Cuppens Frederic Cuppens Sylvain Lamprier
Deep Reinforcement Learning (DRL) is an approach for training autonomous
agents across various complex environments. Despite its significant performance
in well known environments, it remains susceptible to minor conditions
variations, raising concerns about its reliability in real-world applications.
To improve usability, DRL must demonstrate trustworthiness and robustness. A
way to improve robustness of DRL to unknown changes in the conditions is
through Adversarial Training, by training the agent against well suited
adversarial attacks on the dynamics of the environment. Addressing this
critical issue, our work presents an in-depth analysis of contemporary
adversarial attack methodologies, systematically categorizing them and
comparing their objectives and operational mechanisms. This classification
offers a detailed insight into how adversarial attacks effectively act for
evaluating the resilience of DRL agents, thereby paving the way for enhancing
their robustness.