  Recent studies show that Deep Reinforcement Learning (DRL) models are
vulnerable to adversarial attacks, which attack DRL models by adding small
perturbations to the observations. However, some attacks assume full
availability of the victim model, and some require a huge amount of
computation, making them less feasible for real world applications. In this
work, we make further explorations of the vulnerabilities of DRL by studying
other aspects of attacks on DRL using realistic and efficient attacks. First,
we adapt and propose efficient black-box attacks when we do not have access to
DRL model parameters. Second, to address the high computational demands of
existing attacks, we introduce efficient online sequential attacks that exploit
temporal consistency across consecutive steps. Third, we explore the
possibility of an attacker perturbing other aspects in the DRL setting, such as
the environment dynamics. Finally, to account for imperfections in how an
attacker would inject perturbations in the physical world, we devise a method
for generating a robust physical perturbations to be printed. The attack is
evaluated on a real-world robot under various conditions. We conduct extensive
experiments both in simulation such as Atari games, robotics and autonomous
driving, and on real-world robotics, to compare the effectiveness of the
proposed attacks with baseline approaches. To the best of our knowledge, we are
the first to apply adversarial attacks on DRL systems to physical robots.
