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README.md

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+---
+license: mit
+task_categories:
+  - reinforcement-learning
+tags:
+  - world-model
+  - nes
+  - super-mario-bros
+  - death-data
+size_categories:
+  - 10K<n<100K
+---
+
+# SMB Death Data (Noisy TAS)
+
+Training data containing **death sequences** for Super Mario Bros world model.
+
+## Dataset Description
+
+- **89,295 frames** from noisy TAS playthroughs
+- Noise injected at 10 different points (9%, 18%, 27%... through game)
+- 5% noise rate (random button modifications)
+- Captures deaths at ALL stages of the game (World 1 through 8)
+
+## Why This Data?
+
+Clean TAS data only shows optimal play - Mario never dies. This dataset adds:
+- Goomba collisions → death
+- Pit falls → death  
+- Koopa collisions → death
+- Missed jumps → death
+- Failed timing → death
+
+At every stage of the game, not just World 1-1.
+
+## Data Format
+
+Same as smb-worldmodel-data:
+```python
+data = np.load("frame_000000.npz")
+frame = data['frame']   # (224, 256, 3) uint8
+action = data['action'] # (8,) float32
+```
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import zipfile
+
+path = hf_hub_download(
+    repo_id="DylanRiden/smb-death-data",
+    filename="smb_death_data.zip",
+    repo_type="dataset"
+)
+
+with zipfile.ZipFile(path, 'r') as z:
+    z.extractall("./death_data")
+```
+
+## Combine with Clean Data
+
+For training, mix with clean TAS data:
+- [DylanRiden/smb-worldmodel-data](https://huggingface.co/datasets/DylanRiden/smb-worldmodel-data) - 118k clean frames
+- This repo - 89k death frames
+- **Total: 207k frames**
+
+## License
+
+MIT