Challenges and countermeasures for adversarial attacks on deep reinforcement learning

Challenges and countermeasures for adversarial attacks on deep reinforcement learning

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...

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Bibliographic Details
Main Authors: Ilahi, Inaam, Usama, Muhammad, Qadir, Junaid, Janjua, Muhammad Umar, Al-Fuqaha, Ala, Hoang, Dinh Thai, Niyato, Dusit
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Adversarial Machine Learning
Cyber-Security
Online Access:https://hdl.handle.net/10356/163971

Internet

https://hdl.handle.net/10356/163971

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