It is important to transmit data reliably, and efficiently in vehicular networks. Existing works usually study routing strategies, and cooperative scheduling to improve the efficiency of transmission. However, the data transmission remains inefficient because of the lack of full use of communication resources. The transmission is unreliable because information cannot be completely transmitted to the destination vehicles. Moreover, the increasing number of connected vehicles, and the limitation of available communication resources make task scheduling challenging in vehicular networks. In this work, we propose Cluster-enabled Cooperative Scheduling based on Reinforcement Learning (CCSRL) to improve the communication efficiency, and reliability of vehicular networks, with the goal of maximizing the information capacity. In particular, we leverage the stability to select a cluster head vehicle to enhance data transmission efficiency, and a reinforcement learning-based auxiliary transmission is further designed to guarantee the reliable communication among vehicles. The experimental results demonstrate that the performance of the proposed scheduling algorithm, especially the performance of the packet delivery ratio, and node packet loss ratio, is better than that of the state-of-the-art algorithm.

中文翻译：

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基于强化学习的高机动车辆网络集群协同调度

在车载网络中可靠、高效地传输数据非常重要。现有的工作通常研究路由策略和协作调度以提高传输效率。然而，由于缺乏充分利用通信资源，数据传输仍然效率低下。传输是不可靠的，因为信息不能完全传输到目标车辆。此外，连接车辆数量的增加以及可用通信资源的限制使得车辆网络中的任务调度具有挑战性。在这项工作中，我们提出了基于强化学习（CCSRL）的集群协同调度，以提高车载网络的通信效率和可靠性，以最大化信息容量为目标。特别是，我们利用稳定性选择簇头车辆来提高数据传输效率，并进一步设计了基于强化学习的辅助传输，以保证车辆之间的可靠通信。实验结果表明，所提出的调度算法的性能，尤其是包投递率和节点丢包率的性能优于最新算法。