  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.
