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 DNN structure for the WPMEC, inclduing training structure and test structure, implemented based on Tensorflow 1.x.
- memoryTF2.py: Implemented based on Tensorflow 2.
- memoryPyTorch.py: Implemented based on PyTorch.
optimization.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, implemented based on Tensorflow 1.x
- mainTF2.py: Implemented based on Tensorflow 2.
- mainPyTorch.py: Implemented based on PyTorch.
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

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. If you code with Tenforflow 2 or PyTorch, run mainTF2.py or mainPyTorch.py, respectively.
For more DROO demos:
- Laternating-weight WDs, run the file, demo_alternate_weights.py
- ON-OFF WDs, run the file, demo_on_off.py
- Remember to respectively edit the import MemoryDNN code from
```
  from memory import MemoryDNN
```
  to
```
  from memoryTF2 import MemoryDNN
```
  or
```
  from memoryPyTorch import MemoryDNN
```
  if you are using Tensorflow 2 or PyTorch.