rusyadiramli / DROO

A Deep Reinforcement Learning Approach for Online Offloading in Wireless Powered Mobile Edge Computing Networks

DROO

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Python code to reproduce our works on Wireless-powered Mobile-Edge Computing [1], which uses the wireless channel gains as the input and the binary computing mode selection results as the output of a deep neural network (DNN). It includes:

• memory.py: the DNN structure for the WPMEC, inclduing training structure and test structure

• data: all data are stored in this subdirectory, includes:

  • data_#.mat: training and testing data sets, where # = {10, 20, 30} is the user number

• main.py: run this file for DROO, including setting system parameters

• demo_alternate_weights.py: run this file to evaluate the performance of DROO when WDs' weights are alternated

• demo_on_off.py: run this file to evaluate the performance of DROO when some WDs are randomly turning on/off

About our works

1. Liang Huang, Suzhi Bi, and Ying-jun Angela Zhang, Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks, on arxiv:1808.01977.

About authors

• Liang HUANG, lianghuang AT zjut.edu.cn

• Suzhi BI, bsz AT szu.edu.cn

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

Required packages

• Tensorflow

• numpy

• scipy

How the code works

• For DROO algorithm, run the file, main.py

• For DROO demo with laternating-weight WDs, run the file, demo_alternate_weights.py

• For DROO demo with ON-OFF WDs, run the file, demo_on_off.py