The growing complexity and capacity demands for mobile networks necessitate innovative techniques for optimizing resource usage. Meanwhile, recent breakthroughs have brought Reinforcement Learning (RL) into the domain of continuous control of real-world systems. As a step towards RL-based network control, this paper introduces a new framework for benchmarking the performance of an RL agent in network environments simulated with ns-3. Within this framework, we demonstrate that an RL agent without domain-specific knowledge can learn how to efficiently adjust Radio Access Network (RAN) parameters to match offline optimization in static scenarios, while also adapting on the fly in dynamic scenarios, in order to improve the overall user experience. Our proposed framework may serve as a foundation for further work in developing workflows for designing RL-based RAN control algorithms.

翻译：移动网络日益复杂,能力要求日益高涨,这就需要有优化资源使用的创新技术。与此同时,最近的一些突破将加强学习(RL)带入了持续控制现实世界系统的领域。作为向基于RL的网络控制迈出的一步,本文件提出了一个新的框架,用于在以 ns-3 模拟的网络环境中对RL代理器的性能进行基准测试。在此框架内,我们证明没有具体领域知识的RL代理器可以学习如何有效地调整无线电接入网络的参数,使其在静态情景下与离线优化相匹配,同时在动态情景下进行调整,以改进总体用户经验。我们提议的框架可以作为进一步发展基于RL的RAN控制算法工作流程的基础。