Hybrid Reinforcement Learning for Efficient Training of a Mobile Robot

Abstract

Sample effective and stable training remains a key challenge in reinforcement learning (RL), especially for real-world applications such as mobile robot control where data collection is time-consuming and failures may be hazardous.Building on the residual reinforcement learning paradigm, this work presents, to the best of our knowledge, one of the first detailed physical studies of a residual Soft Actor-Critic (SAC) controller for camera-based lane following on a mobile robot. We combine an established stable, but sub-optimal lateral P-controller with a regularized SAC agent in a hybrid architecture. The classical controller provides baseline stability and rapid initial learning, while the RL agent learns residual corrections to improve performance. We employ a PID-inspired reward function and quadratic policy output regularization to ensure smooth control actions and effective sim-to-real transfer.The hybrid controller design enables rapid training convergence, requiring only a few epochs and outperforming the pure RL approach by two orders of magnitude in sample efficiency. This enables efficient hyperparameter tuning in simulation and opens the door to future learning directly on physical robots. Fine-tuning with only a few dozen real-world laps achieved robust transfer to the physical robot, maintaining the same architecture and hyperparameters. The method generalized effectively to new scenarios, such as lane changes.