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| license: cc-by-nc-4.0 |
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| This repository contains model checkpoints for [**Generative OOD-regularized Model-based Policy Optimization (GORMPO)**](https://openreview.net/forum?id=3KDoYUU2CU) on author-created sparse medium-expert level D4RL datasets from the Gym environment: halfcheetah, walker2d and hopper. |
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| Each folder contains density model checkpoints (KDE, VAE, RealNVP, Diffusion, NeuralODE) and policy checkpoints for MBPO-based (mbpo_based) and MOBILE-based (mobile_based) GORMPO. Each policy folder contains policies that are trained with each density model's reward penalty. |
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| ## Usage |
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| Clone the [GORMPO codebase](https://github.com/Rose-STL-Lab/GORMPO), download the models, and follow the instructions in the README. See the datasets [here](https://huggingface.co/datasets/aysin/sparse_medium_expert_D4RL). |
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| ## Citation |
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| If you find this repository useful for your research, please cite: |
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| ```bibtex |
| @inproceedings{tumay2026gormpo, |
| title={Generative {OOD}-regularized Model-based Policy Optimization}, |
| author={Tumay, Aysin and Huang, Jiahe and Jortberg, Elise and Yu, Rose}, |
| booktitle={Advances in Neural Information Processing Systems}, |
| year={2026}, |
| url={https://openreview.net/forum?id=3KDoYUU2CU} |
| } |
| ``` |
