Update README.md

README.md

@@ -1,9 +1,28 @@
+---
+dataset_name: MoSim Dataset
+language:
+- en
+license: mit
+tags:
+- reinforcement-learning
+- world-model
+- dynamics-simulation
+- motion
+- state-action
+- npz
+task_categories:
+- reinforcement-learning
+pretty_name: MoSim Dataset
+size_categories:
+- 1K<n<10K
+---
+
 # 🗂️ MoSim Dataset
 
 Official release of the dataset from the paper:  
 **[Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning](https://arxiv.org/pdf/2504.07095)**  
 
-This dataset contains sequential state-action trajectories for **MoSim (Neural Motion Simulator)** world model training.  
+This dataset contains sequential **state-action trajectories** for training and evaluating **MoSim (Neural Motion Simulator)** world models.  
 All trajectories are collected **from random policies** in classical control and locomotion environments.
 
 ---
@@ -42,4 +61,15 @@ Each `.npz` file contains:
 > ⚡ For manipulation control tasks like `Panda`, only joint angles and velocities are provided.  
 > ⚡ For locomotion tasks like `Humanoid` or `Go2`, full root DOF and velocities are included.
 
+---
+
+## 📥 Usage Example
+
+```python
+import numpy as np
+
+data = np.load("acrobot_random.npz")
+states = data["states"]     # shape: (num_episodes, 1000, state_dim)
+actions = data["actions"]   # shape: (num_episodes, 1000, action_dim)
 
+print(states.shape, actions.shape)