Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving

TL;DR

This paper introduces a human guidance-based reinforcement learning framework with a novel prioritized experience replay mechanism, improving learning efficiency and robustness in autonomous driving tasks.

Contribution

It proposes a new prioritized experience replay mechanism that incorporates human guidance and a behavior model to reduce human workload in autonomous driving reinforcement learning.

Findings

Enhanced learning efficiency and performance compared to state-of-the-art methods

Robustness in autonomous driving tasks demonstrated through experiments

Behavior model effectively mimics human actions to reduce workload

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 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.