Challenges and countermeasures for adversarial attacks on deep reinforcement learning

• Main Authors: Ilahi, Inaam, Usama, Muhammad, Qadir, Junaid, Janjua, Muhammad Umar, Al-Fuqaha, Ala, Hoang, Dinh Thai, Niyato, Dusit
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Computer science and engineering; Adversarial Machine Learning; Cyber-Security
• 在線閱讀: https://hdl.handle.net/10356/163971

Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to its ability to achieve high performance in a range of environments with little manual oversight. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life crit...