The official implementation of Offline Transition Modeling via Contrastive Energy Learning.
Download and install the main code from Energy Transition Model. The implementation is based on OfflineRL-Kit.
git clone https://github.com/Ruifeng-Chen/Energy-Transition-Models.git
cd Energy-Transition-Models
pip install -e .
Train an Energy Transition Model:
python run_example/train_etm.py
Evaluate policies within the Energy Transition Model:
python run_example/mbope.py --load_etm_path "your_ETM_path"
Perform offline policy optimization using EMPO within the learned energy transition models:
python run_example/run_empo.py --load_etm_path "ETM_path_1" "ETM_path_2" "ETM_path_3" "ETM_path_4" "ETM_path_5"
If you use this implementation in your work, please cite us with the following:
@inproceedings{
Energy Transition Model,
title={Offline Transition Modeling via Contrastive Energy Learning},
author={Ruifeng Chen and Chengxing Jia and Zefang Huang and Tian-Shuo Liu and Xu-Hui Liu and Yang Yu},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=dqpg8jdA2w}
}