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README.md

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+---
+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")
+
+# 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) - 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}
+}
+```