Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving

This paper proposes an uncertainty-aware framework for reinforcement learning in autonomous driving, leveraging expert advice to guide exploration safely while avoiding long-term dependence. It employs adaptive thresholds for advice triggering and a commitment-cooldown strategy to regulate guidance, demonstrating improved performance in CARLA simulations.