发表机构
Universidade Federal de Minas Gerais; University of Ottawa; University of Antwerp - imec（米纳斯吉拉斯联邦大学; 渥太华大学; 安特卫普大学-imec）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于MAPPO的多智能体强化学习框架，用于TSN车载边缘网络的队列级调度，联合优化服务顺序与时隙，显著降低延迟并提高可靠性。
AI 中文摘要
车载边缘计算（VEC）通过将计算和网络资源靠近车辆部署，支持延迟敏感型应用。然而，现有方法往往忽视了具有异构和动态延迟需求的共存服务之间的网络竞争。虽然时间敏感网络（TSN）提供有界延迟通信，但传统和基于强化学习的调度器难以适应高度动态的车载环境及队列间依赖。为解决这些局限，我们提出了一种多智能体强化学习（MARL）方法，用于TSN使能的VEC中的队列级调度。每个TSN队列被分配一个自主智能体，该智能体联合学习队列服务顺序和时隙持续时间，以在速度相关的延迟需求下最小化截止时间错失。我们采用多智能体近端策略优化（MAPPO）来实现协调且自主的调度决策。与单智能体、多智能体及非学习基线相比，评估显示MAPPO在不同流量配置下均提供稳健性能。与集中式单智能体方法相比，它降低了高达66.2%的服务延迟，并提高了高达271.8%的可靠性。此外，与基于紧迫性的启发式方法不同，MAPPO确保均衡调度，同时与其他MARL方法相比实现更低的推理时间。
英文摘要
Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles. However, existing approaches often overlook network contention among co-located services with heterogeneous and dynamic latency requirements. While time-sensitive networking (TSN) provides bounded-latency communication, conventional and reinforcement learning-based schedulers struggle to adapt to highly dynamic vehicular environments and inter-queue dependencies. To address these limitations, we propose a multi-agent reinforcement learning (MARL) approach for queue-level scheduling in TSN-enabled VEC. Each TSN queue is assigned an autonomous agent that jointly learns the queue service order and time-slot duration to minimize deadline misses under speed-dependent latency requirements. We employ multi-agent proximal policy optimization (MAPPO) to enable coordinated yet autonomous scheduling decisions. Evaluation against single-agent, multi-agent, and non-learning-based baselines shows that MAPPO provides robust performance across different traffic profiles. Compared with centralized single-agent methods, it reduces service latency by up to 66.2% and improves reliability by up to 271.8%. Furthermore, unlike urgency-based heuristics, MAPPO ensures balanced scheduling while achieving lower inference times compared to other MARL methods.
