LearningCC：一种在线学习的拥塞控制方法
LearningCC: An online learning approach for congestion control
摘要：
近年来，学术界和工业界的研究人员都致力于开发新的拥塞控制方法。本文提出了一种基于强化学习的CC算法，该算法采用强化学习的方法解决了拥塞控制问题。与使用固定策略调整拥塞窗口不同，端点可以选择几种选项。预测最佳选择是一项艰巨的任务。每一个选项都被映射为一个强盗机器的手臂。端点可以通过试错法学习确定最优选择。在ns3平台上进行实验，通过与其他基准算法的比较，验证了LearningCC的有效性。实验结果表明，该算法比基于损耗的算法具有更低的传输时延。特别是，我们发现LearningCC在遭受随机丢失的链路中有显着的改善。
英文摘要：
Recently, much effort has been devoted by researchers from both academia and industry to develop novel congestion control methods. LearningCC is presented in this letter, in which the congestion control problem is solved by reinforce learning approach. Instead of adjusting the congestion window with fixed policy, there are serval options for an endpoint to choose. To predict the best option is a hard task. Each option is mapped as an arm of a bandit machine. The endpoint can learn to determine the optimal choice through trial and error method. Experiments are performed on ns3 platform to verify the effectiveness of LearningCC by comparing with other benchmark algorithms. Results indicate it can achieve lower transmission delay than loss based algorithms. Especially, we found LearningCC makes significant improvement in link suffering from random loss.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
