Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks

2026-10-07

AI Summary

This study addresses the challenges of heterogeneous service contention and the inability of traditional scheduling to accommodate dynamic latency in vehicular edge networks. We propose a multi-agent reinforcement learning (MARL) queue-level scheduling scheme tailored for time-sensitive networking (TSN). To our knowledge, this work is the first to introduce the MAPPO algorithm into TSN-based vehicular edge network queue scheduling, where individual queue agents collaboratively learn service sequencing and time-slot allocation to minimize deadline misses, thereby enabling autonomous coordinated decision-making with reduced inference overhead. Experimental results demonstrate that, compared with centralized approaches, the proposed scheme reduces service latency by 66.2% and improves reliability by 271.8%, while maintaining robust performance across diverse traffic scenarios.

Abstract

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.

Problem

• Vehicular Edge Computing
• Time-Sensitive Networking
• Network Contention
• Deadline-Aware Scheduling
• Dynamic Latency Requirements

Innovation

• Multi-Agent Reinforcement Learning
• Time-Sensitive Networking
• Vehicular Edge Computing
• MAPPO
• Queue-Level Scheduling