Multi-Agent Reinforcement Learning Enhances V2X Communication Channel Selection

Jul 14, 2026

Researchers applied the MAPPO multi-agent reinforcement learning algorithm to the problem of selecting between cellular Uu, NR-V2X PC5 sidelink, or both for vehicle-to-everything (V2X) communication. In urban scenario simulations, this approach improved the on-time delivery ratio from 0.508 to 0.535 in single-vehicle settings and from 0.548 to 0.567 when all vehicles used the learned policy, while halving training time compared to a deep reinforcement learning baseline. The improvements were most pronounced for advanced V2X applications such as cooperative driving and shared perception.

Why it matters: This work shows that multi-agent reinforcement learning can effectively manage hybrid V2X communication to meet diverse latency and reliability needs, which is important for future autonomous and cooperative vehicle systems.

Full story at: arXiv Multiagent Systems ↗