用多智能体强化学习动态调度边缘计算中的实时流量，提升延迟表现。
Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application
- 每个队列作为独立智能体，通过HAPPO算法协同优化服务
- 平均帧等待时间降低26.8%，最坏情况延迟减少16.8%
- 适合高动态、多流并发的实时应用如扩展现实
时间敏感网络（TSN）正越来越多地融入移动边缘计算（MEC），以支持扩展现实（XR）等对延迟要求严苛的应用。然而，现有TSN调度方案主要依赖静态优化或基于固定流量模式的集中式学习模型，在动态环境中效果受限。实际MEC环境常存在多个共存且随时间演化的XR流量，形成复杂的队列间依赖关系，当前调度器难以捕捉。为此，本文提出一种多智能体强化学习（MARL）框架用于TSN调度，将每个TSN队列建模为自治智能体，并采用异构智能体近端策略优化（HAPPO）算法显式建模智能体间依赖关系，联合优化各队列的服务交付。仿真结果表明，该方法在动态XR驱动的MEC场景中，平均帧等待时间最多降低26.8%，最坏情况延迟约减少16.8%，验证了其有效性。
原文摘要 · Abstract (English)
Time-sensitive networking (TSN) is increasingly integrated into mobile edge computing (MEC) to support applications with stringent latency requirements, such as extended reality (XR). However, existing TSN scheduling solutions predominantly rely on static optimization techniques or centralized learning models that are based on fixed traffic patterns, limiting their effectiveness in dynamic environments. In practice, MEC environments often host multiple co-located XR traffic flows whose characteristics evolve over time, creating complex inter-queue dependencies that current schedulers fail to capture. Addressing these challenges requires adaptive, decentralized scheduling mechanisms capable of coordinating multiple TSN queues under varying traffic conditions. To this end, this paper proposes a multi-agent reinforcement learning (MARL) framework for TSN scheduling, where each TSN queue is modeled as an autonomous agent. The Heterogeneous-Agent Proximal Policy Optimization (HAPPO) algorithm is employed to explicitly model inter-agent dependencies and jointly optimize service delivery across queues. The simulation results demonstrate that the proposed approach reduces average frame waiting times by up to 26.8% and worst-case delays by approximately 16.8%, highlighting its effectiveness in dynamic XR-driven MEC scenarios.
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