Safe Reinforcement Learning Using Robust Action Governor

• URL: http://arxiv.org/abs/2102.10643v1
• Date: Sun, 21 Feb 2021 16:50:17 GMT
• Title: Safe Reinforcement Learning Using Robust Action Governor
• Authors: Yutong Li, Nan Li, H. Eric Tseng, Anouck Girard, Dimitar Filev, Ilya Kolmanovsky
• Abstract summary: Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. In this paper, we introduce a framework for safe RL that is based on integration of an RL algorithm with an add-on safety supervision module. We illustrate this proposed safe RL framework through an application to automotive adaptive cruise control.
• Score: 6.833157102376731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. This hinders the applications of RL to real-world control problems, especially to those for safety-critical systems. In this paper, we introduce a framework for safe RL that is based on integration of an RL algorithm with an add-on safety supervision module, called the Robust Action Governor (RAG), which exploits set-theoretic techniques and online optimization to manage safety-related requirements during learning. We illustrate this proposed safe RL framework through an application to automotive adaptive cruise control.

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