This is a jax implementation of PDS on Datasets for Deep Data-Driven Reinforcement Learning (D4RL), the corresponding paper is The provable benefits of unsupervised data sharing for offline reinforcement learning.

For experiments on D4RL, our code is implemented based on IQL:

$ python3 train_data_sharing.py --env_name=walker2d-expert-v2 --source_name=walker2d-random-v2 --config=configs/mujoco_config.py --data_share=learn  --target_split=0.05  --source_split=0.1

If you find this open source release useful, please reference in your paper (it is our honor):

@article{hu2023provable,
  title={The provable benefits of unsupervised data sharing for offline reinforcement learning},
  author={Hu, Hao and Yang, Yiqin and Zhao, Qianchuan and Zhang, Chongjie},
  journal={arXiv preprint arXiv:2302.13493},
  year={2023}
}

• If you have any questions, please contact me: [email protected].