  In this study, we leverage the deliberate and systematic fault-injection
capabilities of an open-source benchmark suite to perform a series of
experiments on state-of-the-art deep and robust reinforcement learning
algorithms. We aim to benchmark robustness in the context of continuous action
spaces -- crucial for deployment in robot control. We find that robustness is
more prominent for action disturbances than it is for disturbances to
observations and dynamics. We also observe that state-of-the-art approaches
that are not explicitly designed to improve robustness perform at a level
comparable to that achieved by those that are. Our study and results are
intended to provide insight into the current state of safe and robust
reinforcement learning and a foundation for the advancement of the field, in
particular, for deployment in robotic systems.
