论文标题
基于深度强化学习的认知路线
Towards Cognitive Routing based on Deep Reinforcement Learning
论文作者
论文摘要
路由是网络基础结构稳定运行的关键功能之一。如今，网络流量量的快速增长和服务要求的变化要求比以前更多的智能路由方法。为此，我们提出了一个基于深度强化学习（DRL）的认知路由和实施方法的定义。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，我们设计并实施了基于DDPG的路由算法。示例网络拓扑结构的仿真结果表明，基于DDPG的路由算法比OSPF和随机重量算法实现更好的性能。它证明了认知路由对未来网络的初步可行性和潜在优势。
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.
