LittleWebCat / DRL-Base-EMS

DRL-Base-EMS for HEVs

DRL-Base-EMS

DRL-Base-EMS for HEVs

Getting Started

The Vehicle Env is OpenAI-Gym like Env, so it needs to work with Gym Env files.

• You can simply install the base Gym library, use with the following command:

  $ pip install gym

  More information about Gym Env can find at https://github.com/openai/gym

The Algorithms library from Tianshou is currently hosted on PyPI and conda-forge. It requires Python >= 3.6.

• You can simply install Tianshou from PyPI with the following command:

  $ pip install tianshou

  More information about Tianshou library can find at https://github.com/thu-ml/tianshou

Setup

1. Get ready with Gym Env and Tianshou library
2. Download the Vehicle_Env file under DRL-Base-EMS
3. Open the Gym Env file of your workspace with suffix like \gym\envs\
4. Add all three content of \DRL-Base-EMS\Vehicle_Env\classic_control\ to \gym\envs\classic_control\
5. Change the content of the \gym\envs\classic_control\__init__.py and \gym\envs\__init__.py by the instruction in \DRL-Base-EMS\Vehicle_Env\__init__.py
6. Now, you can try the different DRL Algorithms in your workspace.

Acknowledgement

We acknowledge the following repositories that greatly shaped our implementation:

• https://github.com/thu-ml/tianshou for providing popular DRL Algorithms in PyTorch and operating guide. Please cite their work if you also find their code useful to your project:

@article{tianshou,
  title={Tianshou: A Highly Modularized Deep Reinforcement Learning Library},
  author={Weng, Jiayi and Chen, Huayu and Yan, Dong and You, Kaichao and Duburcq, Alexis and Zhang, Minghao and Su, Hang and Zhu, Jun},
  journal={arXiv preprint arXiv:2107.14171},
  year={2021}
}

Citation

If you find our code useful to your work, please consider citing our paper:

@article{DRL-EMS,
  title={A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle},
  author={Hanchen Wang, Bin Xu, Yiming Ye, Jiangfeng Zang},
  journal={https://www.sciencedirect.com/science/article/pii/S0360544222033837},
  year={2022}
}

Contact

If you have any questions, please create an issue in this repository or contact Hanchen Wang ([e-mail])