metarl-offloading: A Python repository from linkpark

System environment

Unbuntu 16.04

Preinstall Package

sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev

Create Conda Environment

conda create --name tf-1.15 anaconda python=3.6
conda activate tf-1.15

Install Tensorflow-1.15 GPU or CPU

pip install tensorflow-gpu==1.15

pip install tensorflow==1.15

Install Third-party Python Pakage

pip install gym
pip install graphviz
pip install pydotplus
pip install pyprind
pip install mpi4py

Start Meta Training:

python meta_trainer.py

All the hyperparameters are defined in meta_trainer.py including the log file and save path of the trained model.

Start Meta Evaluation:

After training, you will get the meta model. In order to fast adapt the meta model for new learning tasks in MEC, we need to conduct fine-tuning steps for the trained meta moodel.

python meta_evaluator.py

The training might take long time because of the large training set. All the training results and evaluation results can be found in the log file.

If you like this research, please cite this paper:

@article{wang2020fast,
  title={Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning},
  author={Wang, Jin and Hu, Jia and Min, Geyong and Zomaya, Albert Y and Georgalas, Nektarios},
  journal={IEEE Transactions on Parallel and Distributed Systems},
  volume={32},
  number={1},
  pages={242--253},
  year={2020},
  publisher={IEEE}
}