Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control strategies. Research so far has primarily considered RL interfaces which block the sender while an agent considers its next action. This is largely an artifact of building on top of frameworks designed for RL in games (e.g. OpenAI Gym). However, this does not translate to real-world networking environments, where a network sender waiting on a policy without sending data leads to under-utilization of bandwidth. We instead propose to formulate congestion control with an asynchronous RL agent that handles delayed actions. We present MVFST-RL, a scalable framework for congestion control in the QUIC transport protocol that leverages state-of-the-art in asynchronous RL training with off-policy correction. We analyze modeling improvements to mitigate the deviation from Markovian dynamics, and evaluate our method on emulated networks from the Pantheon benchmark platform. The source code is publicly available at https://github.com/facebookresearch/mvfst-rl.
@article{arxiv.1910.04054,
title = {MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions},
author = {Viswanath Sivakumar and Olivier Delalleau and Tim Rocktäschel and Alexander H. Miller and Heinrich Küttler and Nantas Nardelli and Mike Rabbat and Joelle Pineau and Sebastian Riedel},
journal= {arXiv preprint arXiv:1910.04054},
year = {2021}
}