论文标题
智能运输系统的深入强化学习：一项调查
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey
论文作者
论文摘要
最新技术改进提高了运输质量。新的数据驱动方法为所有基于控制的系统，例如运输，机器人技术，物联网和电源系统提供了新的研究方向。将数据驱动的应用与运输系统相结合在最近的运输应用中起关键作用。在本文中，调查了最新的基于深的加固学习（RL）的交通控制应用程序。具体而言，详细讨论了基于（深）RL的流量信号控制（TSC）应用程序，这些应用程序已在文献中进行了广泛研究。全面讨论了TSC的不同问题公式，RL参数和仿真环境。在文献中，还有一些使用深RL模型研究的自动驾驶应用程序。我们的调查通过对应用程序类型，控制模型和研究算法进行分类，总结了该领域的现有作品。最后，我们讨论了有关基于RL的深层运输应用的挑战和开放问题。
Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.
