*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](memory.py): the DNN structure for the WPMEC, inclduing training structure and test structure, implemented based on [Tensorflow 1.x](https://www.tensorflow.org/install/pip).
-[memoryTF2.py](memoryTF2.py): Implemented based on [Tensorflow 2](https://www.tensorflow.org/install).
-[memoryPyTorch.py](memoryPyTorch.py): Implemented based on [PyTorch](https://pytorch.org/get-started/locally/).
-[optimization.py](optimization.py): solve the resource allocation problem
-[data](./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](main.py): run this file for DROO, including setting system parameters, implemented based on [Tensorflow 1.x](https://www.tensorflow.org/install/pip)
-[mainTF2.py](mainTF2.py): Implemented based on [Tensorflow 2](https://www.tensorflow.org/install). Run this file for DROO if you code with Tensorflow 2.
-[mainPyTorch.py](mainPyTorch.py): Implemented based on [PyTorch](https://pytorch.org/get-started/locally/). Run this file for DROO if you code with PyTorch.
-[demo_alternate_weights.py](demo_alternate_weights.py): run this file to evaluate the performance of DROO when WDs' weights are alternated
-[demo_on_off.py](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](https://ieeexplore.ieee.org/document/8771176),” IEEE Trans. Mobile Compt., vol. 19, no. 11, pp. 2581-2593, November 2020.
```
@ARTICLE{huang2020DROO,
author={Huang, Liang and Bi, Suzhi and Zhang, Ying-Jun Angela},
journal={IEEE Transactions on Mobile Computing},
title={Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks},
year={2020},
month={November},
volume={19},
number={11},
pages={2581-2593},
doi={10.1109/TMC.2019.2928811}
}
```
## About authors
-[Liang HUANG](https://scholar.google.com/citations?user=NifLoZ4AAAAJ), lianghuang AT zjut.edu.cn
-[Suzhi BI](https://scholar.google.com/citations?user=uibqC-0AAAAJ), bsz AT szu.edu.cn
-[Ying Jun (Angela) Zhang](https://scholar.google.com/citations?user=iOb3wocAAAAJ), yjzhang AT ie.cuhk.edu.hk
## Required packages
- Tensorflow
- numpy
- scipy
## How the code works
- For DROO algorithm, run the file, [main.py](main.py). If you code with Tenforflow 2 or PyTorch, run [mainTF2.py](mainTF2.py) or [mainPyTorch.py](mainPyTorch.py), respectively. The original DROO algorithm is coded based on [Tensorflow 1.x](https://www.tensorflow.org/install/pip). If you are fresh to deep learning, please start with [Tensorflow 2](https://www.tensorflow.org/install) or [PyTorch](https://pytorch.org/get-started/locally/), whose codes are much cleaner and easier to follow.
- For more DROO demos:
- Laternating-weight WDs, run the file, [demo_alternate_weights.py](demo_alternate_weights.
- ON-OFF WDs, run the file, [demo_on_off.py](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.
### DROO is illustrated here for single-slot optimization. If you tend to apply DROO for multiple-slot continuous control problems, please refer to our [LyDROO](https://github.com/revenol/LyDROO) project.
