Reinforcement Learning with Model-Based Static Output Feedback for Vehicle Lateral Control

This paper investigates Reinforcement Learning (RL) for model-based static output feedback, with a focus on vehicle lateral control applications such as lane-keeping and lane-change maneuvers. Emphasizing stability during learning to prevent unsafe actions, the study reviews recent RL advancements and compares policy iteration (PI) and policy search (PS) methods in terms of convergence speed and stability. A two-degree-of-freedom bicycle model is employed to validate the proposed PI algorithm for static output feedback, highlighting its faster convergence compared to PS and its competitive performance against traditional Linear Quadratic Regulator (LQR) control. Results demonstrate the practicality and efficiency of RL for stable real-time vehicle control.