Variational-Quantum-Circuits-DeepReinforcementLearning

NEWS We start a non-commericial interview series: a first talk with Sam and his experience working VQC-DRL since 2018.

Feel free to check the videos, if you are interested in the stories behind. Part 1 Video and Part 2 Video.

This is the numerical code for the article:
Variational Quantum Circuits for Deep Reinforcement Learning (first released in Aug. 2019)

• This work awarded the Xanadu AI software competition 2019 research track first prize. News

We also sincerely thank the supports from Xanadu AI to provide PennyLane, which is a great value to the quantum AI community.

Requirements

with pip/conda install

pennylane
pytorch
matplotlib
qiskit
gym
numpy

Run Code

• OpenAI Frozen Lake (please also refer to the parameters )

python Code/QML_DQN_FROZEN_LAKE.py

• Cognitive Radio Game (network-simulator 3 style)

python Code/QML_DQN_NS3.py

References

If you find this work helps your research or use the code, please consider to cite our official reference. Thank you.

@article{chen2020variational,
  title={Variational quantum circuits for deep reinforcement learning},
  author={Chen, Samuel Yen-Chi and Yang, Chao-Han Huck and Qi, Jun and Chen, Pin-Yu and Ma, Xiaoli and Goan, Hsi-Sheng},
  journal={IEEE Access},
  year={2020},
  publisher={IEEE}
}