Kinetix-Offline

3 billion expert transitions across 11 million unique physics-based tasks.

Overview

This dataset contains offline expert trajectories collected in Kinetix, a JAX-based 2D rigid-body physics environment where tasks are procedurally generated. Every task shares the same goal: make the green and blue objects touch, without green touching red. The agent acts by applying torques via motors and forces via thrusters.

Specialist PPO agents were trained independently per procedurally generated task (i.e., level), and only successful trajectories from solvable levels are included (~50% of all generated levels are solvable).

We have ~3B transitions from over 11M unique tasks.

Dataset Splits

Datasets are named {policy_steps}/{size}, where policy_steps is the number of RL training steps used per specialist agent and size is the environment complexity (small, medium, large).

Expert Training Steps	Size	Unique Levels	Transitions	Size on Disk
1M	s	5.98M	1.53B	123 GB
1M	m	3.45M	884M	98 GB
1M	l	1.05M	268M	82 GB
10M	s	637k	163M	12 GB
10M	m	422k	108M	11 GB
Total		11.5M	~3B	326 GB

Data Format

Data is stored as zarr archives. Each batch has shape (batch_size, T, *dims) with T=256 and is returned as an ActionEnvStateMask object:

Field	Shape	Description
action	(B, T, A)	Expert action at each timestep
env_state	(B, T, ...)	Full simulator state (use to re-render in any modality)
action_mask	(B, T, A)	Which action dimensions are active (motors/thrusters present in this level)
done	(B, T)	Episode termination flags
mask	(B, T)	Always True — dataset contains only successful trajectories

Because the full env_state is stored, you can render observations at training time in any modality (symbolic graph or pixels) without storing raw frames.

Usage

Downloading

# Entire dataset (~326 GB)
hf download mbeukman/Kinetix-Offline --repo-type dataset --local-dir ./data

# Single split, e.g. medium-size 1M-step experts (~98 GB)
hf download mbeukman/Kinetix-Offline --repo-type dataset --local-dir ./data --include "1M/m/*"

# Single split, e.g. medium-size 10M-step experts (~11 GB)
hf download mbeukman/Kinetix-Offline --repo-type dataset --local-dir ./data --include "10M/m/*"

Replace 1M/m with any {policy_steps}/{size} combination from the table above.

Loading Data

from kinetix.data import TrajectoryDatasetManager

traj_manager = TrajectoryDatasetManager(
    dataset_dir="/path/to/traj_data",
    batch_size=64,           # number of trajectories per batch
)
batch = traj_manager.load_next_batch()  # shape (64, T, *dims)

See examples/example_data_loading.py for a full runnable example including GIF rendering.

Rendering Pixel Observations

Because the raw environment state is stored, you can render frames in any observation modality at training time without storing raw pixels:

import jax
from kinetix.environment import EnvParams, static_env_params_from_size
from kinetix.render import make_render_pixels

static_env_params = static_env_params_from_size("m")  # match your downloaded split
renderer = jax.jit(make_render_pixels(EnvParams(), static_env_params))

# Render a full batch of trajectories: (B, T, H, W, C)
frames = jax.vmap(jax.vmap(renderer))(batch.env_state)

Behaviour Cloning

A full BC training script is included in the Kinetix repository:

python3 experiments/offline_bc.py dataset_dir=/path/to/data env_size=m

Configuration lives in configs/offline_bc.yaml.

Why Use This Dataset?

Massive task diversity: With 10M+ unique levels, this dataset makes it possible to study how offline agent performance scales with task diversity, and what challenges emerge when learning across millions of tasks.

Dynamic rendering: Raw environment state is stored rather than pre-rendered frames, so the full 3B-transition dataset fits in 326 GB. The rendering function is specified at runtime, meaning the same data can train symbolic or pixel-based agents simply by swapping the renderer.

White-box evaluation: Stored environment states allow online evaluation from any point in a trajectory, on training levels, unseen levels from the same distribution, or the hand-designed benchmark set.

Citation

If you use this dataset, please cite the Kinetix paper:

@article{matthews2024kinetix,
  title={Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks},
  author={Michael Matthews and Michael Beukman and Chris Lu and Jakob Foerster},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025},
  url={https://arxiv.org/abs/2410.23208}
}

Acknowledgements

Compute for this work was provided by the Isambard-AI National AI Research Resource under the project "FLAIR 2025 Moonshot Projects". Thanks to Alex Goldie and Jarek Liessen for useful discussions.