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revenol
140 Stars 83 Forks MIT License 45 Commits 3 Opened issues

Description

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

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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:

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

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, demoalternateweights.py
    • ON-OFF WDs, run the file, demoonoff.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. If you are fresh to deep learning, please start with Tensorflow 2 or PyTorch, whose codes are much cleaner and easier to follow.

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