Smart and connected mobility systems rely on 5G edge infrastructure to support real-time communication, control, and service differentiation. Achieving this requires adaptive resource management mechanisms that can react to rapidly changing traffic conditions. In this paper, we propose RL-Loop, a closed-loop reinforcement learning framework for real-time CPU resource control in 5G network slicing environments supporting connected mobility services. RL-Loop employs a Proximal Policy Optimization (PPO) agent that continuously observes slice-level key performance indicators and adjusts edge CPU allocations at one-second granularity on a real testbed. The framework leverages real-time observability and feedback to enable adaptive, software-defined edge intelligence. Experimental results suggest that RL-Loop can reduce average CPU allocation by over 55% relative to the reference operating point while reaching a comparable quality-of-service degradation region. These results indicate that lightweight reinforcement learning--based feedback control can provide efficient and responsive resource management for 5G-enabled smart mobility and connected vehicle services.

翻译：智能互联出行系统依赖5G边缘基础设施来支撑实时通信、控制及服务差异化。实现这一目标需要能够快速响应动态变化的流量条件的自适应资源管理机制。本文提出RL-循环——一种用于支持互联出行服务的5G网络切片环境中实时CPU资源控制的闭环强化学习框架。该框架采用近端策略优化代理，持续观测切片级关键性能指标，并在真实测试平台上以秒级粒度调整边缘CPU分配。该框架利用实时观测与反馈实现自适应软件定义边缘智能。实验结果表明，相较于基准运行点，RL-循环可在达到可比服务质量退化区域的同时，将平均CPU分配降低超过55%。这些结果说明，轻量级强化学习反馈控制能够为5G赋能智能出行及网联车辆服务提供高效、灵敏的资源管理。