Datasets:

DylanRiden
/

smb-death-data

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:

data = np.load("frame_000000.npz")
frame = data['frame']   # (224, 256, 3) uint8
action = data['action'] # (8,) float32

Usage

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 - 118k clean frames
This repo - 89k death frames
Total: 207k frames

License

MIT