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