Zhuzzq / EdgeFed-MARL-MEC

EdgeFed H-MAAC: Edge Federated Heterogeneous Multi-agent Actor-Critic

This repository contains a gym module for UAV-assisted MEC environment simulation and a TensorFlow implementation of EdgeFed H-MAAC framework.

Zhu Z, Wan S, Fan P, et al. Federated Multiagent Actor–Critic Learning for Age Sensitive Mobile-Edge Computing[J]. IEEE Internet of Things Journal, 2021, 9(2): 1053-1067.

Zhu Z, Wan S, Fan P, et al. An Edge Federated MARL Approach for Timeliness Maintenance in MEC Collaboration[C]//2021 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2021: 1-6.

Run

• To simulate the MEC systems in the paper, standard gym modules are implemented by MEC_env/mec_def.py and MEC_env/mec_env.py.
• An edge-federated actor-critic RL framework with mixed policies, abbreviated as EdgeFed H-MAAC, is developed in MAAC_agent.py.
• A mixed DDPG based algorithm AC_agent.py is also implemented as a baseline.
• Run *_run.py to test the algorithms in the simulated MEC system.

References

• If you find the codes useful, please cite the following in your manuscript:

@article{zhu2021federated,
  title={Federated Multiagent Actor--Critic Learning for Age Sensitive Mobile-Edge Computing},
  author={Zhu, Zheqi and Wan, Shuo and Fan, Pingyi and Letaief, Khaled B},
  journal={IEEE Internet of Things Journal},
  volume={9},
  number={2},
  pages={1053--1067},
  year={2021},
  publisher={IEEE}
}

@inproceedings{zhu2021edge,
  title={An Edge Federated MARL Approach for Timeliness Maintenance in MEC Collaboration},
  author={Zhu, Zheqi and Wan, Shuo and Fan, Pingyi and Letaief, Khaled B},
  booktitle={2021 IEEE International Conference on Communications Workshops (ICC Workshops)},
  pages={1--6},
  year={2021},
  organization={IEEE}
}

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