Double Entropy Reinforcement Learning: Achieving Optimal Outcomes Despite Imperfect Teacher Guidance

Reinforcement learning (RL) has become a key method for decision-making in autonomous vehicles, particularly in tasks like path planning and obstacle avoidance. However, RL often struggles with sparse or delayed reward signals, which can hinder learning. A promising solution is incorporating a teacher-student framework, where the agent learns from a teacher. While these methods can accelerate learning, they risk sub-optimal outcomes if the teacher's guidance is flawed. This paper introduces Double Entropy Reinforcement Learning (DERL), which enables the student to initially rely on the teacher and gradually shift towards independent exploration as it surpasses the teacher's performance. Experiments show that DERL outperforms existing teacher-student algorithms in learning efficiency and effectiveness, reducing reliance on imperfect guidance.