In this article, we introduce a novel dynamic controller assignment algorithm targeting connected vehicle services and applications, also known as Internet of Vehicles (IoV). The proposed approach considers a hierarchically distributed control plane, decoupled from the data plane, and uses vehicle location and control traffic load to perform controller assignment dynamically. We model the dynamic controller assignment problem as a multi-agent Markov game and solve it with cooperative multi-agent deep reinforcement learning. Simulation results using real-world vehicle mobility traces show that the proposed approach outperforms existing ones by reducing control delay as well as packet loss.

中文翻译：

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通过多智能体深度强化学习在软件定义的车辆互联网中进行动态控制器分配

在本文中，我们介绍了一种针对连接的车辆服务和应用程序的新型动态控制器分配算法，也称为车联网（IoV）。所提出的方法考虑了与数据平面分离的分层分布的控制平面，并使用车辆位置和控制交通负荷来动态执行控制器分配。我们将动态控制器分配问题建模为多主体马尔可夫博弈，并通过协作式多主体深度强化学习对其进行求解。使用现实世界的车辆移动轨迹进行的仿真结果表明，该方法通过减少控制延迟以及丢包率，优于现有方法。