Adaptive Design Parameter Determination for Control Barrier Functions using Reinforcement Learning

This paper introduces an innovative method to improve control system performance through the adaptive determination of the design parameter (γ) in Control Barrier Functions (CBFs) using reinforcement learning (RL). Conventional approaches with a fixed γparameter often fall short in dynamic environments. Our approach utilizes RL to dynamically adjust γbased on real-time feedback, allowing for more adaptable and efficient responses to varying conditions. Simulations in Adaptive Cruise Control (ACC) scenarios show that our adaptive γselection significantly enhances the system's ability to maintain safety and performance. This method has promising implications for various safety-critical applications.