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.
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LyDROOwithTF2conv.py: run this file for LyDROO with CNN implemented on Tenforflow 2.
- 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, 2021, doi:10.1109/TWC.2021.3085319.
@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={},
number={},
pages={1-1},
doi={10.1109/TWC.2021.3085319}
}
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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
- For LyDROO algorithm, run the file, LyDROO.py or LyDROOwithTF2conv.py. In general, LyDROO (CNN) converges much faster than LyDROO (DNN).