README.md

---
dataset_info:
  features:
    - name: id
      dtype: string
    - name: game
      dtype: string
    - name: trial_id
      dtype: int32
    - name: episode_id
      dtype: int32
    - name: chunk_idx
      dtype: int32
    - name: frame_start
      dtype: int32
    - name: action
      dtype: string
    - name: action_ints
      dtype: string
    - name: score
      dtype: int32
    - name: reward_sum
      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
  - action-chunking
size_categories:
  - 1M<n<10M
---

# TESS-Atari Stage 1 (15Hz)

Human gameplay demonstrations from Atari games with **action chunking**, 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 | ~1.3M |
| Observation Rate | 5 Hz |
| Action Rate | 15 Hz (3 actions per observation) |
| Format | Lumine-style action tokens |

## Why Action Chunking?

VLA models run at ~5 Hz inference speed, but Atari runs at 15 Hz (with frame_skip=4). Action chunking predicts 3 actions at once, matching the game's effective action rate while accommodating slower model inference.

```
Observation (5 Hz) → VLA → 3 Actions (executed at 15 Hz)
```

## Games Included

Alien, Asterix, BankHeist, Breakout, DemonAttack, Freeway, Frostbite, Hero, MsPacman, RoadRunner, Seaquest

## Action Format

```
<|action_start|> RIGHT ; RIGHT ; FIRE <|action_end|>
<|action_start|> LEFT ; LEFT ; LEFT <|action_end|>
<|action_start|> NOOP ; UP ; UPFIRE <|action_end|>
```

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique sample ID: `{game}_{trial}_{chunk}` |
| `game` | string | Game name (lowercase) |
| `trial_id` | int | Human player trial number |
| `episode_id` | int | Episode within trial (-1 if unknown) |
| `chunk_idx` | int | Chunk sequence number |
| `frame_start` | int | First frame index of this chunk |
| `action` | string | Lumine-style chunked action token |
| `action_ints` | string | Raw ALE codes comma-separated: "4,4,1" |
| `score` | int | Score at chunk start |
| `reward_sum` | int | Total reward over 3 frames |
| `gaze_positions` | string | Eye tracking from first frame |
| `image_bytes` | bytes | PNG of first frame in chunk |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("TESS-Computer/atari-vla-stage1-15hz")

# Get a sample
sample = ds["train"][0]
print(sample["action"])  # <|action_start|> RIGHT ; RIGHT ; FIRE <|action_end|>

# Parse individual actions
actions = sample["action_ints"].split(",")  # ["4", "4", "1"]

# Decode image
from PIL import Image
from io import BytesIO
img = Image.open(BytesIO(sample["image_bytes"]))
```

## Evaluation

Use with [DIAMOND](https://diamond-wm.github.io/) world models (frame_skip=4). Execute the 3 predicted actions sequentially at each observation step.

## Related

- [5Hz variant](https://huggingface.co/datasets/TESS-Computer/atari-vla-stage1-5hz) - Single action per observation (simpler but slower)
- [Lumine AI](https://www.lumine-ai.org/) - Inspiration for VLA architecture and action chunking
- [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}
}
```