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

Catherine Glossop, Jacopo Panerati, Amrit Krishnan, Zhaocong Yuan, Angela P. Schoellig

Keywords: reinforcement learning, robust control, continuous control, robotics, safety, benchmarks

TL;DR: Benchmarking Continuous Control Robust RL with Disturbance Injection

Abstract:

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.