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

arXiv:2403.00420v1 Announce Type: new
Abstract: 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 …

abstract adversarial adversarial attacks agents applications arxiv attacks autonomous autonomous agents concerns cs.ai cs.lg environments performance reinforcement reinforcement learning reliability robust survey through training type usability world