Timezone: »
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)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Oral: A Deep Reinforcement Learning Perspective on Internet Congestion Control »
Wed. Jun 12th 09:35 -- 09:40 PM Room Hall B
More from the Same Authors
-
2023 Poster: Learning Control by Iterative Inversion »
Gal Leibovich · Guy Jacob · Or Avner · Gal Novik · Aviv Tamar -
2023 Poster: ContraBAR: Contrastive Bayes-Adaptive Deep RL »
Era Choshen · Aviv Tamar -
2023 Poster: TGRL: An Algorithm for Teacher Guided Reinforcement Learning »
Idan Shenfeld · Zhang-Wei Hong · Aviv Tamar · Pulkit Agrawal -
2022 Poster: Unsupervised Image Representation Learning with Deep Latent Particles »
Tal Daniel · Aviv Tamar -
2022 Spotlight: Unsupervised Image Representation Learning with Deep Latent Particles »
Tal Daniel · Aviv Tamar -
2020 Poster: Hallucinative Topological Memory for Zero-Shot Visual Planning »
Kara Liu · Thanard Kurutach · Christine Tung · Pieter Abbeel · Aviv Tamar -
2020 Poster: Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning »
Tom Jurgenson · Or Avner · Edward Groshev · Aviv Tamar -
2019 Poster: Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN »
dror freirich · Tzahi Shimkin · Ron Meir · Aviv Tamar -
2019 Oral: Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN »
dror freirich · Tzahi Shimkin · Ron Meir · Aviv Tamar -
2017 Poster: Constrained Policy Optimization »
Joshua Achiam · David Held · Aviv Tamar · Pieter Abbeel -
2017 Talk: Constrained Policy Optimization »
Joshua Achiam · David Held · Aviv Tamar · Pieter Abbeel