Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks
To address these limitations, we propose a multi-agent reinforcement learning (MARL) approach for queue-level scheduling in TSN-enabled VEC.
ProofPaper ↗
Key points
- Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles.
- 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.
- 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.
- Evaluation against single-agent, multi-agent, and non-learning-based baselines shows that MAPPO provides robust performance across different traffic profiles.
Sources (1)
- [1]Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge NetworksarXiv (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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