Decentralized Stochastic Proximal Gradient Descent with Variance Reduction over Time-varying Networks

no code implementations • 20 Dec 2021 • Xuanjie Li, Yuedong Xu, Jessie Hui Wang, Xin Wang, John C. S. Lui

By transforming our decentralized algorithm into a centralized inexact proximal gradient algorithm with variance reduction, and controlling the bounds of error sequences, we prove that DPSVRG converges at the rate of $O(1/T)$ for general convex objectives plus a non-smooth term with $T$ as the number of iterations, while DSPG converges at the rate $O(\frac{1}{\sqrt{T}})$.

Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning

no code implementations • 9 May 2019 • Xinyu You, Xuanjie Li, Yuedong Xu, Hui Feng, Jin Zhao, Huaicheng Yan

Packet routing is one of the fundamental problems in computer networks in which a router determines the next-hop of each packet in the queue to get it as quickly as possible to its destination.

Decision Making Q-Learning +2

Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling

no code implementations • 20 Nov 2017 • Weijia Chen, Yuedong Xu, Xiaofeng Wu

Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years.

reinforcement-learning Reinforcement Learning (RL) +1