This is an upgraded version of the DROO algorithm, where the memory.py is rewritten based on PyTorch.

The following description follows from DROO

xmind文件是应用程序“xmind”才能打开的，绘画思维导图的专有文件。

DROO

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

Python code to reproduce our DROO algorithm for Wireless-powered Mobile-Edge Computing [1], which uses the time-varying wireless channel gains as the input and generates the binary offloading decisions. It includes:

• memory.py: the DROO structure for the WPMEC, inclduing building DNN, storing data sample, Training DNN, and generating quantized binary offloading decisions.

• optimizatin.py: Solve the resource allocation problem.

• 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

Cite this work

1. L. Huang, S. Bi, and Y. J. Zhang, “Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks,” IEEE Trans. Mobile Compt., DOI:10.1109/TMC.2019.2928811, Jul. 2019.

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