# Internet Congestion Control via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1810.03259 · HTML: https://mlsanity.com/paper/1810.03259/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Barbarians at the Gate: How AI is Upending Systems Research](https://mlsanity.com/paper/2510.06189/) (2025)
- [Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization](https://mlsanity.com/paper/2102.11055/) (2021)
- [Glia: A Human-Inspired AI for Automated Systems Design and Optimization](https://mlsanity.com/paper/2510.27176/) (2025)
- [Impact of RoCE Congestion Control Policies on Distributed Training of DNNs](https://mlsanity.com/paper/2207.10898/) (2022)
- [Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning](https://mlsanity.com/paper/2106.06931/) (2021)
- [Demystifying Reinforcement Learning in Time-Varying Systems](https://mlsanity.com/paper/2201.05560/) (2022)
- [Let the Barbarians In: How AI Can Accelerate Systems Performance Research](https://mlsanity.com/paper/2512.14806/) (2025)

## Abstract (arXiv)

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.