Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to decide the workload allocation and schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy. Second, an artificial intelligence (AI) based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution in a complex urban transportation network. By our approach, the service cost, which includes computing service latency and service failure penalty, can be minimized via the optimal workload assignment and server selection in collaborative computing. Simulation results show that the proposed AI-based collaborative computing approach can adapt to a highly dynamic environment with outstanding performance.

中文翻译：

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车载网络协同边缘计算的深度强化学习

移动边缘计算 (MEC) 是一项很有前途的技术，可支持关键任务车辆应用，例如智能路径规划和安全应用。在本文中，开发了一种协作边缘计算框架，以减少计算服务延迟并提高车载网络的服务可靠性。首先，提出了一种任务分区和调度算法（TPSA）来决定工作负载分配，并在给定计算卸载策略的情况下调度卸载到边缘服务器的任务的执行顺序。其次，开发了一种基于人工智能 (AI) 的协作计算方法，以确定车辆的任务卸载、计算和结果交付策略。具体来说，卸载和计算问题被表述为马尔可夫决策过程。采用深度强化学习技术，即深度确定性策略梯度，在复杂的城市交通网络中寻找最优解。通过我们的方法，可以通过协同计算中的最佳工作负载分配和服务器选择来最小化服务成本，包括计算服务延迟和服务失败惩罚。仿真结果表明，所提出的基于人工智能的协同计算方法能够以优异的性能适应高度动态的环境。可以通过协同计算中的最佳工作负载分配和服务器选择来最小化。仿真结果表明，所提出的基于人工智能的协同计算方法能够以优异的性能适应高度动态的环境。可以通过协同计算中的最佳工作负载分配和服务器选择来最小化。仿真结果表明，所提出的基于人工智能的协同计算方法能够以优异的性能适应高度动态的环境。