Update README.md

README.md

@@ -1,3 +1,36 @@
 ---
 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}
+}
+```