FreeworkEarth/Quantum-Multi-Agent-Reinforcement-Learning

Quantum Multi-agent Reinforcement Learning (QMARL)

Quantum-Multi-agent-Reinforcement-Learning (QMARL)

Installation

Install pytorch & torchquantum to operate QMARL.

git clone https://github.com/mit-han-lab/torchquantum.git
cd torchquantum
pip install --editable .

Usage

git clone https://github.com/WonJoon-Yun/Quantum-Multi-Agent-Reinforcement-Learning.git
cd Quantum-Multi-Agent-Reinforcement-Learning
python trainer.py

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT

Citations

If this QMARL framework helps your academic/industrial research, please cite this article Paper. This work is accepted to 42nd IEEE International Conference on Distributed Computing Systems (ICDCS 2022)!

@article{yun2022quantum,
  title={Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design},
  author={Yun, Won Joon and Kwak, Yunseok and Kim, Jae Pyoung and Cho, Hyunhee and Jung, Soyi and Park, Jihong and Kim, Joongheon},
  journal={CoRR},
  volume={abs:2203.10443},
  year={2022},
  month={March}
}