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In this study, we leverage the deliberate and systematic fault-injectioncapabilities of an open-source benchmark suite to perform a series ofexperiments on state-of-the-art deep and robust reinforcement learningalgorithms. We aim to benchmark robustness in the context of continuous actionspaces -- crucial for deployment in robot control. We find that robustness ismore prominent for action disturbances than it is for disturbances toobservations and dynamics. We also observe that state-of-the-art approachesthat are not explicitly designed to improve robustness perform at a levelcomparable to that achieved by those that are. Our study and results areintended to provide insight into the current state of safe and robustreinforcement learning and a foundation for the advancement of the field, inparticular, for deployment in robotic systems.

Characterising the Robustness of Reinforcement Learning for Continuous Control using Disturbance Injection