TESS-Computer/atari-vla-stage1-5hz

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
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

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 world models on the Atari 100k benchmark.

Related

• 15Hz variant - 3 actions per observation for faster gameplay
• Lumine AI - Inspiration for VLA architecture
• DIAMOND - World model for evaluation

Citation

@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}
}