Multi-Kernel Enhanced Receding-Horizon Reinforcement Learning for Steering Control of Intelligent Vehicles

Achieving optimal control in the path-tracking of intelligent vehicles is crucial for enhancing driving performance, yet it remains challenging due to model uncertainties and nonlinear dynamics. Reinforcement learning (RL), as a class of approximated optimal control methods, has gained attention for tackling complex problems with uncertain or nonlinear dynamics. However, effective feature representation and online learning efficiency are two major issues that persist in RL methods for adaptive optimal control. To address these challenges, this paper proposes a Receding-horizon Multi-kernel Reinforcement Learning (RM-RL) algorithm, which integrates an efficient online learning mechanism with improved feature representations. RM-RL operates within a receding-horizon control framework that facilitates online policy optimization and deployment. Meanwhile, employing a multi-kernel features learning approach for the actor-critic structure and the dynamics model further improves learning efficiency and generalization. Besides, the convergence and closed-loop stability are analyzed in depth. Real-world experiments conducted on the Hongqi E-HS3 vehicle and simulations on the CarSim platform demonstrate the effectiveness and the superiority of RM-RL over advanced comparative methods.