droo's Introduction

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

Liang Huang, Suzhi Bi, and Ying-jun Angela Zhang, Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks, IEEE Transactions on Mobile Computing, early access. Preview available 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

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