Q-Advantage Integrated Human-Guided Reinforcement Learning for Safe End-to-End Autonomous Driving

Reinforcement learning (RL) is a promising approach for end-to-end autonomous driving, but its practical deployment remains challenging due to low sample efficiency and sensitivity to reward design. To address these challenges, this study presents a novel Q-advantage integrated human-guided reinforcement learning (QIHG-RL) framework that effectively combines the strengths of machine learning and human expertise. The QIHG-RL framework features: 1) an ensemble Q-advantage function that aggregates