---
license: mit
task_categories:
  - video-classification
  - reinforcement-learning
tags:
  - world-model
  - nes
  - super-mario-bros
  - game-ai
size_categories:
  - 100K<n<1M
---

# SMB World Model Training Data

Training data for a Super Mario Bros world model using the Titan memory architecture.

## Dataset Description

- **118,166 frames** from 8 TAS (Tool-Assisted Speedrun) playthroughs
- **Frame size:** 224x256x3 (RGB)
- **Action space:** 8 buttons [Up, Down, Left, Right, A, B, Start, Select]
- **Format:** Compressed `.npz` files (each contains frame + action bundled together)
- **Total size:** ~293MB (compressed)

## TAS Files Used

| TAS File | Description |
|----------|-------------|
| smb_all_items | Collects all items |
| smb_low_percent | Minimal item collection |
| smb_max_coins | Maximum coins |
| smb_max_score | Maximum score |
| smb_min_a_presses | Minimal A button presses |
| smb_scoreless | Zero score run |
| smb_warpless | No warp zones |
| smb_warps | Using warp zones |

## Data Format

Each `.npz` file contains:
```python
data = np.load("frame_000000.npz")
frame = data['frame']   # shape: (224, 256, 3), dtype: uint8
action = data['action'] # shape: (8,), dtype: float32
```

Action order: `[Up, Down, Left, Right, A, B, Start, Select]`

## Usage

```python
from huggingface_hub import hf_hub_download
import zipfile

# Download
path = hf_hub_download(
    repo_id="DylanRiden/smb-worldmodel-data",
    filename="smb_frames.zip",
    repo_type="dataset"
)

# Extract
with zipfile.ZipFile(path, 'r') as z:
    z.extractall("./nes_data")
```

## Collection Method

- Emulator: FCEUX with Lua scripting
- Frame skip: Every 4th frame (15fps from 60fps)
- Menu skip: First 250 frames skipped
- Real-time conversion to bundled `.npz` format

## License

MIT