LyDROO

Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

PyTorch code to reproduce LyDROO algorithm [1], which is an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. It applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems and solves each subproblem via DROO algorithm. It includes:

• memory.py: the codes of Deep Reinforcement Learning based on fully connected neural networks (DNN) implemented on PyTorch.

• memoryTF2conv.py: the codes of Deep Reinforcement Learning based on convolutional neural networks (CNN) implemented on Tenforflow 2.

• ResourceAllocation: Algorithms for resource allocation.

• LyDROO.py: run this file for LyDROO with DNN implemented on PyTorch, namely LyDROO (DNN).

• LyDROOwithTF2conv.py: run this file for LyDROO with CNN implemented on Tenforflow 2, namely LyDROO (CNN).

Cite this work

1. Suzhi Bi, Liang Huang, and Ying-jun Angela Zhang, ``Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks'', IEEE Transactions on Wireless Communications, vol. 20, no. 11, pp. 7519-7537, November 2021.

@ARTICLE{bi2021LyDROO,  
  author={Bi, Suzhi and Huang, Liang and Wang, Hui and Zhang, Ying-Jun Angela},
  journal={IEEE Transactions on Wireless Communications},
  title={Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks},
  year={2021},
  volume={20},
  number={11},
  pages={7519-7537},
  doi={10.1109/TWC.2021.3085319}
}

About authors

• Suzhi BI, bsz AT szu.edu.cn

• Liang HUANG, lianghuang AT zjut.edu.cn

• Ying Jun (Angela) Zhang, yjzhang AT ie.cuhk.edu.hk

How the code works

• For LyDROO (DNN), run the file, LyDROO.py. Please make sure that PyTorch has been successfully installed before execution.

Training Loss	Average Data Queue

• For LyDROO (CNN), run the file, LyDROOwithTF2conv.py. Please make sure that Tensorflow 2 has been successfully installed before execution.

Training Loss	Average Data Queue

• Remarks:
  • You may arrive at similar but different results since both wireless channels and data arrivals are randomly generated on lines 130-139 of LyDROO.py or LyDROOwithTF2conv.py.
  • LyDROO (DNN) requires more training frames before convegence, e.g., increase the time frame variable 'n' from 10000 to 20000 on line 65 of LyDROO.py.
  • In general, LyDROO (CNN) converges much faster than LyDROO (DNN).