1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchAlyssa Hugheshttps://www.microsoft.com/en-us/research/people/live-comalyssahughes-ms/OpenNetLab: Open Platform for RL-based Congestion Control for Real-Time Communications - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="37HJyZ7ov1"><a href="https://www.microsoft.com/en-us/research/publication/opennetlab-apnet/">OpenNetLab: Open Platform for RL-based Congestion Control for Real-Time Communications</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/opennetlab-apnet/embed/#?secret=37HJyZ7ov1" width="600" height="338" title="“OpenNetLab: Open Platform for RL-based Congestion Control for Real-Time Communications” — Microsoft Research" data-secret="37HJyZ7ov1" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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With the growing importance of real-time communications (RTC), designing congestion control (CC) algorithms for RTC that achieve high network performance and QoE is gaining attention. Recently, data-driven, reinforcement learning (RL)-based CC algorithms for RTC have shown great potential, outperforming traditional rule-based counterparts. However, there are no open platforms tailored for training, evaluation, and validation of the algorithms that can facilitate this emerging research area. We present OpenNetLab, an open platform for fast training, reproducible end-to-end evaluation, and performance validation of RL-based CC algorithms for RTC. Preliminary use cases confirm that OpenNetLab concretely aided the training of novel RL-based CC algorithms for RTC that outperform a well-established rule-based baseline in both network performance and QoE metrics.