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

2024-03-01 10:16:46
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.

Abstract (translated)

深度强化学习(DRL)是一种训练各种复杂环境中自主行动的智能体的方法。尽管在著名环境中表现出色,但它仍然容易受到轻微条件变化的影响,这使得人们对它在现实应用中的可靠性产生担忧。为了提高可用性,DRL必须表现出可靠性、容错性。提高DRL对未知条件变化容错性的方法是通过对抗训练,通过训练智能体对抗适合环境动态的合适对抗攻击。为解决这一关键问题,我们的工作对当代攻击方法进行了深入分析,系统地分类并比较了它们的 objectives 和操作机制。这种分类提供了一个详细了解如何有效评估 DRL 代理的韧性的见解,为提高其韧性和可靠性铺平道路。



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