The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate the collection of traffic data and its interpretation towards alleviating congestion and providing better traffic light control. DRLE operates within the coverage of the edge servers and uses aggregated data from neighboring edge servers to provide city-scale traffic light control. DRLE decomposes the highly complex problem of large area control. into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning. The proposed decentralized reinforcement learning algorithm running at each edge node adapts the traffic lights in real time. We conduct extensive evaluations and demonstrate the superiority of this approach over several state-of-the-art algorithms.

中文翻译：

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DRLE：车联网中交通灯控制边缘的去中心化强化学习

车联网（IoV）实现了车辆和路边单元之间的实时数据交换，从而为缓解市区交通拥堵提供了一个有前景的解决方案。同时，通过有效的交通灯控制进行更好的交通管理也可以通过实现更好的通信环境和减少网络负载来使车联网受益。因此，车联网和高效的交通灯控制可以形成良性循环。边缘计算是一种在网络边缘提供低延迟计算能力的新兴技术，可以进一步提高这个循环的性能。然而，虽然收集的信息很有价值，但尚未为边缘赋能的 IoV 开发出更好的利用和更快的反馈的有效解决方案。为此，我们建议在边缘进行去中心化强化学习，用于车联网 (DRLE) 中的交通灯控制。DRLE 利用车联网的普遍性来加速交通数据的收集及其解释，以缓解拥堵并提供更好的交通灯控制。DRLE 在边缘服务器的覆盖范围内运行，并使用来自相邻边缘服务器的聚合数据来提供城市规模的交通灯控制。DRLE 分解了大面积控制的高度复杂的问题。变成去中心化的多智能体问题。我们用具体的数学推理证明了它的全局最优。在每个边缘节点上运行的所提出的分散强化学习算法实时适应交通灯。