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arXiv · 2210.15199

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

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

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Catherine R. Glossop, Jacopo Panerati, Amrit Krishnan, Zhaocong Yuan, Angela P. Schoellig. 2022-10-27. Characterising the Robustness of Reinforcement Learning for Continuous Control using Disturbance Injection. https://arxiv.org/abs/2210.15199

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