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arXiv · 2403.00420

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
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Abstract

Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments. Despite its significant performance in well-known environments, it remains susceptible to minor condition variations, raising concerns about its reliability in real-world applications. To improve usability, DRL must demonstrate trustworthiness and robustness. A way to improve the robustness of DRL to unknown changes in the environmental conditions and possible perturbations is through Adversarial Training, by training the agent against well-suited adversarial attacks on the observations and the dynamics of the environment. Addressing this critical issue, our work presents an in-depth analysis of contemporary adversarial attack and training methodologies, systematically categorizing them and comparing their objectives and operational mechanisms.

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Lucas Schott, Josephine Delas, Hatem Hajri, Elies Gherbi, Reda Yaich, Nora Boulahia-Cuppens, Frederic Cuppens, Sylvain Lamprier. 2024-03-01. Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey. https://arxiv.org/abs/2403.00420

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