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

Key points

• Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles.1
• 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.1
• 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.1
• Evaluation against single-agent, multi-agent, and non-learning-based baselines shows that MAPPO provides robust performance across different traffic profiles.1

Sources (1)

• [1]Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 11:30 AM

  To address these limitations, we propose a multi-agent reinforcement learning (MARL) approach for queue-level scheduling in TSN-enabled VEC.

  Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles.

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