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A Deep Reinforcement Learning Perspective on Internet Congestion Control
Nathan Jay · Noga H. Rotman · Brighten Godfrey · Michael Schapira · Aviv Tamar

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #45

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 are 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.

Author Information

Nathan Jay (University of Illinois Urbana-Champaign)
Noga H. Rotman (Hebrew University of Jerusalem)
Brighten Godfrey (University of Illinois Urbana-Champaign)
Michael Schapira (Hebrew University of Jerusalem)
Aviv Tamar (Technion)

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