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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}
}
``` |