Official implementation of
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts by
Yu Luo, Tianying Ji, Fuchun Sun, Jianwei Zhang, Huazhe Xu and Xianyuan Zhan
We provide examples on how to train and evaluate OMPO agent.
See below examples on how to train OBAC on a single task.
python main_stationary.py --env_name YOUR_TASKWe recommend using default hyperparameters. See utils/default_config.py for a full list of arguments.
If you find our work useful, please consider citing our paper as follows:
@inproceedings{Luo2024ompo,
title={OMPO: A Unified Framework for RL under Policy and Dynamics Shifts},
author={Yu Luo and Tianjing Ji and Fuchun Sun and Jianwei Zhang and Huazhe Xu and Xianyuan Zhan},
booktitle={International Conference on Machine Learning},
year={2024}
}
Please feel free to participate in our project by opening issues or sending pull requests for any enhancements or bug reports you might have. We’re striving to develop a codebase that’s easily expandable to different settings and tasks, and your feedback on how it’s working is greatly appreciated!
This project is licensed under the MIT License - see the LICENSE file for details. Note that the repository relies on third-party code, which is subject to their respective licenses.