Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

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Deep LearningLyDROO
Overview

LyDROO

Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

PyTorch code to reproduce LyDROO algorithm [1], which is an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. It applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems and solves each subproblem via DROO algorithm. It includes:

About our works

  1. Suzhi Bi, Liang Huang, and Ying-jun Angela Zhang, ``Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks'', IEEE Transactions on Wireless Communications, 2021, doi:10.1109/TWC.2021.3085319.

About authors

  • Suzhi BI, bsz AT szu.edu.cn

  • Liang HUANG, lianghuang AT zjut.edu.cn

  • Ying Jun (Angela) Zhang, yjzhang AT ie.cuhk.edu.hk

How the code works

  • For LyDROO algorithm, run the file, LyDROO.py
Owner
Liang HUANG
Liang HUANG
GitHub Repository
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