DROO:无线供电的移动边缘计算网络中在线计算卸载的深度强化学习

# 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](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). - [mainPyTorch.py](mainPyTorch.py): Implemented based on [PyTorch](https://pytorch.org/get-started/locally/). - [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. ## 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. - For more DROO demos: - Laternating-weight WDs, run the file, [demo_alternate_weights.py](demo_alternate_weights.py) - 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. ### The 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.