机器学习与网络优化

机器学习搞得火热朝天，搞网络的也向跟着热一把。但是能网络能和机器学习扯上关系的，似乎也就是增强学习。这里就对增强学习与网络相关的论文做个整理。论文中结论无一例外，都是很好。

congestion control

QTCP: Adaptive congestion control with reinforcement learning
TCP ex Machina: Computer-Generated Congestion Control
An Experimental Study of the Learnability of Congestion Control
Internet Congestion Control via Deep Reinforcement Learning
TCP-Drinc: Smart Congestion Control Based on Deep Reinforcement Learning
Dynamic TCP Initial Windows and Congestion Control Schemes Through Reinforcement Learning
PCC Vivace: Online-Learning Congestion Control
Delay-Constrained Rate Control for Real-Time Video Streaming with Bounded Neural Network
Multi-Armed Bandit Congestion Control in Multi-Hop Infrastructure Wireless Mesh Networks
Improving TCP Congestion Control with Machine Intelligence
Multi-Armed Bandit in Action: Optimizing Performance in Dynamic Hybrid Networks
Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control
code https://github.com/dcgym/iroko
Dynamic TCP Initial Windows and Congestion Control Schemes Through Reinforcement Learning

A Reinforcement Learning Approach for Multipath TCP Data Scheduling
ReLeS: A Neural Adaptive Multipath Scheduler based on Deep Reinforcement Learning
SmartCC: A Reinforcement Learning Approach for Multipath TCP Congestion Control in Heterogeneous Networks
Experience-driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning
Peekaboo: Learning-based Multipath Scheduling for Dynamic Heterogeneous Environments

D-DASH: A deep Q-learning framework for DASH video streaming
Online learning adaptation strategy for DASH clients
Continual learning improves Internet video streaming
Neural Adaptive Video Streaming with Pensieve
Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streaming
PiTree: Practical Implementation of ABR Algorithms Using Decision Trees
HotDASH: Hotspot Aware Adaptive Video Streaming using Deep Reinforcement Learning (code:https://github.com/SatadalSengupta/hotdash)

QARC: Video Quality Aware Rate Control for Real-Time Video Streaming based on Deep Reinforcement Learning
LEAP: Learning-Based Smart Edge with Caching and Prefetching for Adaptive Video Streaming

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN
code: https://github.com/knowledgedefinednetworking/a-deep-rl-approach-for-sdn-routing-optimization