We present and investigate a novel and timely application domain for deep reinforcement learning (RL): Internet congestion control. Congestion control is the core networking task of modulating traffic sources' data-transmission rates to efficiently utilize network capacity, and is the subject of extensive attention in light of the advent of Internet services such as live video, virtual reality, Internet-of-Things, and more. We show that casting congestion control as RL enables training deep network policies that capture intricate patterns in data traffic and network conditions, and leverage this to outperform the state-of-the-art. We also highlight significant challenges facing real-world adoption of RL-based congestion control, including fairness, safety, and generalization, which arc not trivial to address within conventional RL formalism. To facilitate further research and reproducibility of our results, we present a test suite for RL-guided congestion control based on the OpenAI Gym interface.
|Original language||American English|
|Title of host publication||36th International Conference on Machine Learning, ICML 2019|
|Publisher||International Machine Learning Society (IMLS)|
|Number of pages||10|
|State||Published - 2019|
|Event||36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States|
Duration: 9 Jun 2019 → 15 Jun 2019
|Name||36th International Conference on Machine Learning, ICML 2019|
|Conference||36th International Conference on Machine Learning, ICML 2019|
|Period||9/06/19 → 15/06/19|
Bibliographical noteFunding Information:
We thank Huawei for ongoing support of our research on congestion control. The fourth author is supported by the Israel Science Foundation (ISF).
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