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:
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memory.py: the codes of Deep Reinforcement Learning based on fully connected neural networks (DNN) implemented on PyTorch.
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memoryTF2conv.py: the codes of Deep Reinforcement Learning based on convolutional neural networks (CNN) implemented on Tenforflow 2.
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ResourceAllocation: Algorithms for resource allocation.
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LyDROO.py: run this file for LyDROO with DNN implemented on PyTorch, namely LyDROO (DNN).
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LyDROOwithTF2conv.py: run this file for LyDROO with CNN implemented on Tenforflow 2, namely LyDROO (CNN).
Cite this work
- 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
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Suzhi BI, bsz AT szu.edu.cn
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Liang HUANG, lianghuang AT zjut.edu.cn
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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 |
|---|---|
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- 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 |
|---|---|
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- 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).