论文标题
通过进化策略加速数字双网络优化的深度强化学习
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies
论文作者
论文摘要
新兴网络应用程序的最新增长（例如卫星网络，车辆网络）正在增加管理现代通信网络的复杂性。结果，社区提出了数字双网络（DTN）作为有效网络管理的关键推动者。网络运营商可以利用DTN执行不同的优化任务（例如，流量工程，网络计划）。当应用于解决网络优化问题时，深度加强学习（DRL）显示出高性能。在DTN的背景下，可以利用DRL来解决优化问题，而无需直接影响现实世界的网络行为。但是，DRL随着问题的大小和复杂性而缩小范围很差。在本文中，我们探讨了进化策略（ES）来培训DRL代理以解决路由优化问题的使用。实验结果表明，NSFNET和GEANT2拓扑的ES分别达到了128和6的训练时间。
The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a result, the community proposed the Digital Twin Networks (DTN) as a key enabler of efficient network management. Network operators can leverage the DTN to perform different optimization tasks (e.g., Traffic Engineering, Network Planning). Deep Reinforcement Learning (DRL) showed a high performance when applied to solve network optimization problems. In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior. However, DRL scales poorly with the problem size and complexity. In this paper, we explore the use of Evolutionary Strategies (ES) to train DRL agents for solving a routing optimization problem. The experimental results show that ES achieved a training time speed-up of 128 and 6 for the NSFNET and GEANT2 topologies respectively.
