论文标题
真正的交易：对基于强化学习的挑战和机遇的回顾
The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality
论文作者
论文摘要
交通信号控制（TSC）是一个高风险域，随着交通量在全球的增长而增长。越来越多的作品将加固学习（RL）应用于TSC； RL可以利用大量的流量数据来提高信号效率。但是，基于RL的信号控制器从未部署过。在这项工作中，我们提供了对TSC进行RL之前必须解决的挑战的首次审查。我们专注于涉及（1）检测不确定性的四个挑战，（2）通信的可靠性，（3）合规性和解释性，以及（4）异构道路使用者。我们表明，基于RL的TSC的文献在应对每个挑战方面取得了一些进步。但是，更多的工作应采用系统思维方法，以考虑其他管道组件对RL的影响。
Traffic signal control (TSC) is a high-stakes domain that is growing in importance as traffic volume grows globally. An increasing number of works are applying reinforcement learning (RL) to TSC; RL can draw on an abundance of traffic data to improve signalling efficiency. However, RL-based signal controllers have never been deployed. In this work, we provide the first review of challenges that must be addressed before RL can be deployed for TSC. We focus on four challenges involving (1) uncertainty in detection, (2) reliability of communications, (3) compliance and interpretability, and (4) heterogeneous road users. We show that the literature on RL-based TSC has made some progress towards addressing each challenge. However, more work should take a systems thinking approach that considers the impacts of other pipeline components on RL.
