Multi-agent reinforcement learning improves vehicular edge network scheduling
Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks
Networking and Internet ArchitectureArtificial Intelligence
Summary
Cars need fast and reliable communication to handle different tasks on the road. The authors found that current methods miss deadlines when many tasks compete for network time. They designed a system where many small decision-makers work together to decide which task gets priority and for how long. This approach cuts delays and reduces missed deadlines, making communication more dependable in busy vehicle networks.
What this means in practice
- •For vehicular network engineers: Improve how vehicle communication systems schedule network resources to meet strict timing needs despite changing road traffic.
- •For edge computing platform operators: Enable more reliable and faster service scheduling for applications running close to vehicles by using coordinated autonomous agents.
Authors
Bernardo A. C. Pereira, Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Andreas Gavrielides, Johann M. Marquez-Barja, Daniel F. Macedo
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.