@inproceedings{3f51cae6a16f47638d9e448f8c449423,
title = "OpenNetLab: Open Platform for RL-based Congestion Control for Real-Time Communications",
abstract = "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.",
keywords = "Congestion control, Open platform, Real-time communications, Reinforcement learning",
author = "Jeongyoon Eo and Zhixiong Niu and Wenxue Cheng and Yan, \{Francis Y.\} and Rui Gao and Jorina Kardhashi and Scott Inglis and Michael Revow and Chun, \{Byung Gon\} and Peng Cheng and Yongqiang Xiong",
note = "We thank the reviewers for their valuable comments. We also thank all OpenNetLab contributors (https://opennetlab.org/about) for substantial efforts on building and maintaining the prototype of OpenNetLab. This research was supported by the MSIT(Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program(2021-0-01696) supervised by the IITP(Institute for Information \& Communications Technology Planning \& Evaluation), and another IITP grant funded by the MSIT (No.2015-0-00221).; 6th Asia-Pacific Workshop on Networking, APNet 2022 ; Conference date: 01-07-2022 Through 02-07-2022",
year = "2022",
month = jul,
day = "1",
doi = "10.1145/3542637.3542648",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "70--75",
booktitle = "Proceedings of the 6th Asia-Pacific Workshop on Networking, APNet 2022",
address = "United States",
}