| --- |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: game |
| dtype: string |
| - name: trial_id |
| dtype: int32 |
| - name: episode_id |
| dtype: int32 |
| - name: frame_idx |
| dtype: int32 |
| - name: action |
| dtype: string |
| - name: action_int |
| dtype: int32 |
| - name: score |
| dtype: int32 |
| - name: reward |
| dtype: int32 |
| - name: reaction_time_ms |
| dtype: int32 |
| - name: gaze_positions |
| dtype: string |
| - name: image_bytes |
| dtype: binary |
| license: mit |
| task_categories: |
| - robotics |
| - reinforcement-learning |
| tags: |
| - atari |
| - vla |
| - vision-language-action |
| - imitation-learning |
| - human-demonstrations |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # TESS-Atari Stage 1 (5Hz) |
|
|
| Human gameplay demonstrations from Atari games, formatted for Vision-Language-Action (VLA) model training. |
|
|
| ## Overview |
|
|
| | Metric | Value | |
| |--------|-------| |
| | Source | [Atari-HEAD](https://zenodo.org/records/3451402) | |
| | Games | 11 (overlapping with DIAMOND benchmark) | |
| | Samples | ~4M | |
| | Action Rate | 5 Hz (1 action per observation) | |
| | Format | Lumine-style action tokens | |
|
|
| ## Games Included |
|
|
| Alien, Asterix, BankHeist, Breakout, DemonAttack, Freeway, Frostbite, Hero, MsPacman, RoadRunner, Seaquest |
|
|
| ## Action Format |
|
|
| ``` |
| <|action_start|> FIRE <|action_end|> |
| <|action_start|> LEFT <|action_end|> |
| <|action_start|> RIGHTFIRE <|action_end|> |
| ``` |
|
|
| ## Schema |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `id` | string | Unique sample ID: `{game}_{trial}_{frame}` | |
| | `game` | string | Game name (lowercase) | |
| | `trial_id` | int | Human player trial number | |
| | `episode_id` | int | Episode within trial (-1 if unknown) | |
| | `frame_idx` | int | Frame sequence number | |
| | `action` | string | Lumine-style action token | |
| | `action_int` | int | Raw ALE action code (0-17) | |
| | `score` | int | Current game score | |
| | `reward` | int | Immediate reward | |
| | `reaction_time_ms` | int | Human decision time in ms | |
| | `gaze_positions` | string | Eye tracking data (x,y pairs) | |
| | `image_bytes` | bytes | PNG image of game frame | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TESS-Computer/atari-vla-stage1-5hz-clean") |
| |
| # Get a sample |
| sample = ds["train"][0] |
| print(sample["action"]) # <|action_start|> FIRE <|action_end|> |
| |
| # Decode image |
| from PIL import Image |
| from io import BytesIO |
| img = Image.open(BytesIO(sample["image_bytes"])) |
| ``` |
|
|
| ## Evaluation |
|
|
| Designed for evaluation in [DIAMOND](https://diamond-wm.github.io/) world models on the Atari 100k benchmark. |
|
|
| ## Related |
|
|
| - [15Hz variant](https://huggingface.co/datasets/TESS-Computer/atari-vla-stage1-15hz-clean) - 3 actions per observation for faster gameplay |
| - [Lumine AI](https://www.lumine-ai.org/) - Inspiration for VLA architecture |
| - [DIAMOND](https://diamond-wm.github.io/) - World model for evaluation |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{atarihead2019, |
| title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset}, |
| author={Zhang, Ruohan and others}, |
| year={2019}, |
| url={https://zenodo.org/records/3451402} |
| } |
| ``` |
|
|
