13 November 2019
Industry estimates show that more than 150 exabytes of data per month were sent over the internet in 2018 and this expected to nearly double by 2021. Effective network congestion control strategies are key to keeping the Internet operational at this massive scale. For decades, these systems have been dominated by handcrafted heuristics that can react to dynamic traffic patterns but are not able to learn new ways to anticipate them. Reinforcement Learning (RL) holds the promise of taking proactive steps to reduce network congestion and adapt to varying network scenarios, and yet to our knowledge none has been transferred to a real-world production system. This talk will cover a research platform to advance RL for congestion control and the fundamental challenges involved.