--- license: cc0-1.0 --- # Atari-Asterix Dataset This is a large dataset of **10M video frames** and actions collected from the **Alien** atari environment (Bellemare et al., 2012) in order to train world models. The dataset enables reproducible, large-scale experiments in action-conditioned video prediction. It is meant to be used with [Jasmine](https://github.com/p-doom/jasmine), our JAX-based world modeling codebase. --- ### Dataset Summary - **Environment:** Atari Learning Environment - **Frames:** 10 million - **Resolution:** 84 × 84 - **Format:** [`ArrayRecord`](https://github.com/google/array_record) (for fast I/O) - **Splits:** `train` / `val` / `test` - **License:** [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) --- ## Usage This dataset is part of the [Jasmine](https://github.com/p-doom/jasmine) repository release. Frames were collected during Rainbow (Hessel et al. 2017) agent training. You can find the dataset generation code at https://github.com/p-doom/jasmine/tree/main/data/jasmine_data. The ArrayRecord format enables efficient dataloading using Grain and is optimized for the [Jasmine dataloader](https://github.com/p-doom/jasmine/blob/main/jasmine/utils/dataloader.py). You can download the dataset using the `huggingface-cli` tool. ```bash huggingface-cli download --repo-type dataset p-doom/atari-asterix-dataset --local-dir ``` To start a training run using Jasmine, simply pass the `train` and `val` split to the training script. ```bash python jasmine/baselines/maskgit/train_tokenizer_vqvae.py \ --data_dir /train \ --val_data_dir /val \ ... ``` ## Citation If you use our Atari datasets, please cite our work: ```tex @article{ mahajan2025jasmine, title={Jasmine: A simple, performant and scalable JAX-based world modeling codebase}, author={Mihir Mahajan and Alfred Nguyen and Franz Srambical and Stefan Bauer}, journal = {p(doom) blog}, year={2025}, url={https://pdoom.org/jasmine.html}, note = {https://pdoom.org/blog.html} }