  Deep Reinforcement Learning (DRL) has numerous applications in the real world
thanks to its outstanding ability in quickly adapting to the surrounding
environments. Despite its great advantages, DRL is susceptible to adversarial
attacks, which precludes its use in real-life critical systems and applications
(e.g., smart grids, traffic controls, and autonomous vehicles) unless its
vulnerabilities are addressed and mitigated. Thus, this paper provides a
comprehensive survey that discusses emerging attacks in DRL-based systems and
the potential countermeasures to defend against these attacks. We first cover
some fundamental backgrounds about DRL and present emerging adversarial attacks
on machine learning techniques. We then investigate more details of the
vulnerabilities that the adversary can exploit to attack DRL along with the
state-of-the-art countermeasures to prevent such attacks. Finally, we highlight
open issues and research challenges for developing solutions to deal with
attacks for DRL-based intelligent systems.
