Routing is one of the key functions for stable operation of network infrastructure. Nowadays, the rapid growth of network traffic volume and changing of service requirements call for more intelligent routing methods than before. Towards this end, we propose a definition of cognitive routing and an implementation approach based on Deep Reinforcement Learning (DRL). To facilitate the research of DRL-based cognitive routing, we introduce a simulator named RL4Net for DRL-based routing algorithm development and simulation. Then, we design and implement a DDPG-based routing algorithm. The simulation results on an example network topology show that the DDPG-based routing algorithm achieves better performance than OSPF and random weight algorithms. It demonstrate the preliminary feasibility and potential advantage of cognitive routing for future network.

中文翻译：

---

基于深度强化学习的认知路由

路由是网络基础设施稳定运行的关键功能之一。如今，网络流量的快速增长和服务需求的变化，需要比以往更智能的路由方法。为此，我们提出了认知路由的定义和基于深度强化学习（DRL）的实现方法。为了促进基于 DRL 的认知路由的研究，我们引入了一个名为 RL4Net 的模拟器，用于基于 DRL 的路由算法开发和仿真。然后，我们设计并实现了一个基于 DDPG 的路由算法。在一个示例网络拓扑上的仿真结果表明，基于 DDPG 的路由算法比 OSPF 和随机权重算法实现了更好的性能。它证明了认知路由对未来网络的初步可行性和潜在优势。