Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving
This work addresses efficiency and workload issues in reinforcement learning for autonomous driving, though it appears incremental as it builds on existing human guidance and experience replay concepts.
The paper tackles the challenge of improving reinforcement learning efficiency for autonomous driving by incorporating human guidance, proposing a prioritized experience replay mechanism and behavior model to reduce human workload. Experimental results show advantages in learning efficiency, performance, and robustness compared to state-of-the-art methods.
Reinforcement learning (RL) requires skillful definition and remarkable computational efforts to solve optimization and control problems, which could impair its prospect. Introducing human guidance into reinforcement learning is a promising way to improve learning performance. In this paper, a comprehensive human guidance-based reinforcement learning framework is established. A novel prioritized experience replay mechanism that adapts to human guidance in the reinforcement learning process is proposed to boost the efficiency and performance of the reinforcement learning algorithm. To relieve the heavy workload on human participants, a behavior model is established based on an incremental online learning method to mimic human actions. We design two challenging autonomous driving tasks for evaluating the proposed algorithm. Experiments are conducted to access the training and testing performance and learning mechanism of the proposed algorithm. Comparative results against the state-of-the-art methods suggest the advantages of our algorithm in terms of learning efficiency, performance, and robustness.