| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - reinforcement-learning |
| tags: |
| - offline-rl |
| - d4rl |
| - mujoco |
| - sparse |
| pretty_name: Sparse Medium-Expert D4RL |
| --- |
| |
| # Sparse Medium-Expert D4RL |
|
|
| Sparse versions of the D4RL Gym-MuJoCo **medium-expert** datasets (HalfCheetah, Hopper, Walker2d), introduced in |
| [Generative OOD-regularized Model-based Policy Optimization (GORMPO)](https://openreview.net/forum?id=3KDoYUU2CU). |
|
|
| ## Description |
|
|
| These datasets test offline RL methods under thin state-action coverage, where policies are prone to drifting into |
| out-of-distribution (OOD) regions. For each dataset, we define an "unsafe" region that encloses one mode of the |
| action-norm vs. reward space, and randomly discard 50% of the trajectories passing through it. Overall, this removes |
| about 27.5% (HalfCheetah), 22% (Hopper), and 27% (Walker2d) of the original samples. |
|
|
| We chose the medium-expert level because its mix of behavior quality resembles clinical decision-making data. |
|
|
| ## Citation |
|
|
| ``` |
| @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} |
| } |
| ``` |
