  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.
