基于强化学习的拥塞窗口调整策略研究

Research on Congestion Window Adjustment Strategy Based on Reinforcement Learning

针对网络拥塞控制问题,结合机器学习算法,提出了基于强化学习的拥塞窗口调整(CWARL)策略。首先定义了部分网络知识来表示所感知到的网络拥塞程度,设计了动作集合以确定调整拥塞窗口的幅度,设计了兼顾吞吐量和丢包率的奖励函数。其次提出了基于Q学习的窗口调整策略,通过学习网络特征合理地调整拥塞窗口。最后使用实验评估CWARL策略,实验结果表明,提出的CWARL策略的综合性能优于所对比的拥塞控制策略。

Aiming at the problem of network congestion control, combined with machine learning algorithms, this paper proposes a strategy of Congestion Window Adjustment based on Reinforcement Learning(CWARL). This paper first defines some network knowledge to represent the perceived degree of network congestion, designs actions set to determine the magnitude of the adjustment congestion windows, and designs a reward function to juggle throughput and packet loss rate. Second, this paper proposes a window adjustment strategy based on Q-learning, adjusts reasonably the congestion window by learning network features. Finally, it uses experiments to evaluate the CWARL strategy, and the experimental results show that the overall performance of the proposed CWARL strategy is better than the contrasted congestion control strategy.