Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …

As a promising machine learning tool to handle the accurate pattern recognition from
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …

Network modeling is a key enabler to achieve efficient network operation in future self-
driving Software-Defined Networks. However, we still lack functional network models able to …

Reinforcement learning (RL), which is a class of machine learning, provides a framework by
which a system can learn from its previous interactions with its environment to efficiently …

Recently, we have seen a boom of attempts to improve the operation of networking protocols
using machine learning techniques. The proposed reinforcement learning (RL) based …

Abstract Deep Reinforcement Learning (DRL) has shown a dramatic improvement in
decision-making and automated control problems. Consequently, DRL represents a …

Network planning is critical to the performance, reliability and cost of web services. This
problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's …

对面向2030 的未来网络领域的发展趋势进行了综述. 首先, 介绍了网络体系架构和试验设施领域
的研究进展; 其次, 从网络控制与编排, 网络深度可编程, 网络确定性服务, 网络计算存储一体化 …

Traditional routing protocols employ limited information to make routing decisions, which
can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality …

Network modeling is a critical component for building self-driving Software-Defined
Networks, particularly to find optimal routing schemes that meet the goals set by …