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--- |
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license: mit |
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task_categories: |
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- video-classification |
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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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- game-ai |
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size_categories: |
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- 100K<n<1M |
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--- |
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# SMB World Model Training Data |
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Training data for a Super Mario Bros world model using the Titan memory architecture. |
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## Dataset Description |
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- **118,166 frames** from 8 TAS (Tool-Assisted Speedrun) playthroughs |
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- **Frame size:** 224x256x3 (RGB) |
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- **Action space:** 8 buttons [Up, Down, Left, Right, A, B, Start, Select] |
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- **Format:** Compressed `.npz` files (each contains frame + action bundled together) |
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- **Total size:** ~293MB (compressed) |
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## TAS Files Used |
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| TAS File | Description | |
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|----------|-------------| |
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| smb_all_items | Collects all items | |
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| smb_low_percent | Minimal item collection | |
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| smb_max_coins | Maximum coins | |
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| smb_max_score | Maximum score | |
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| smb_min_a_presses | Minimal A button presses | |
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| smb_scoreless | Zero score run | |
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| smb_warpless | No warp zones | |
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| smb_warps | Using warp zones | |
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## Data Format |
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Each `.npz` file contains: |
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```python |
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data = np.load("frame_000000.npz") |
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frame = data['frame'] # shape: (224, 256, 3), dtype: uint8 |
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action = data['action'] # shape: (8,), dtype: float32 |
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``` |
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Action order: `[Up, Down, Left, Right, A, B, Start, Select]` |
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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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# Download |
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path = hf_hub_download( |
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repo_id="DylanRiden/smb-worldmodel-data", |
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filename="smb_frames.zip", |
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repo_type="dataset" |
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) |
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# Extract |
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with zipfile.ZipFile(path, 'r') as z: |
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z.extractall("./nes_data") |
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``` |
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## Collection Method |
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- Emulator: FCEUX with Lua scripting |
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- Frame skip: Every 4th frame (15fps from 60fps) |
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- Menu skip: First 250 frames skipped |
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- Real-time conversion to bundled `.npz` format |
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## License |
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MIT |
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