Several research works have applied Reinforcement Learning (RL) algorithms to solve the Rate Adaptation (RA) problem in Wi-Fi networks. The dynamic nature of the radio link requires the algorithms to be responsive to changes in link quality. Delays in the execution of the algorithm may be detrimental to its performance, which in turn may decrease network performance. This aspect has been overlooked in the state of the art. In this paper, we present an analysis of common computational delays in RL-based RA algorithms, and propose a methodology that may be applied to reduce these computational delays and increase the efficiency of this type of algorithms. We apply the proposed methodology to an existing RL-based RA algorithm. The obtained experimental results indicate a reduction of one order of magnitude in the execution time of the algorithm, improving its responsiveness to link quality changes.

翻译：已有多个研究工作将强化学习算法应用于解决Wi-Fi网络中的速率自适应问题。无线链路的动态特性要求算法能够快速响应链路质量的变化。算法执行过程中的延迟可能对其性能产生不利影响，进而降低网络性能。这一方面在现有研究中尚未得到充分关注。本文对基于强化学习的速率自适应算法中常见的计算延迟进行了分析，并提出了一种可用于减少这些计算延迟并提高此类算法效率的方法。我们将所提出的方法应用于一个现有的基于强化学习的速率自适应算法。实验结果表明，该算法的执行时间降低了数量级，从而改善了其对链路质量变化的响应能力。