Update README.md

README.md

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 ---
 license: mit
+task_categories:
+  - reinforcement-learning
+tags:
+  - reinforcement-learning
+  - physics
+  - offline-rl
+  - behaviour-cloning
+  - jax
+  - procedural-generation
+size_categories:
+  - 1B<n<10B
 ---
+
+# Kinetix-Offline
+
+**3 billion expert transitions across 11 million unique physics-based tasks.**
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2410.23208"><img src="https://img.shields.io/badge/arxiv-2410.23208-b31b1b" /></a>
+  <a href="https://github.com/FLAIROx/Kinetix"><img src="https://img.shields.io/badge/code-FLAIROx%2FKinetix-black" /></a>
+  <a href="https://kinetix-env.github.io/dataset"><img src="https://img.shields.io/badge/blog-Kinetix%2010M-blue" /></a>
+</p>
+
+## Overview
+
+This dataset contains offline expert trajectories collected in [Kinetix](https://kinetix-env.github.io/), 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 (`s`mall, `m`edium, `l`arge).
+
+| 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](https://zarr.readthedocs.io/) 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
+
+```bash
+# 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
+
+```python
+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`](https://github.com/FLAIROx/Kinetix/blob/main/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:
+
+```python
+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:
+
+```bash
+python3 experiments/offline_bc.py dataset_dir=/path/to/data env_size=m
+```
+
+Configuration lives in [`configs/offline_bc.yaml`](https://github.com/FLAIROx/Kinetix/blob/main/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:
+
+```bibtex
+@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.