Prioritized Experience-Based Reinforcement Learning With Human Guidance for Autonomous Driving

Abstract

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 RL is a promising way to improve learning performance. In this article, a comprehensive human guidance-based RL framework is established. A novel prioritized experience replay mechanism that adapts to human guidance in the RL process is proposed to boost the efficiency and performance of the RL 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.