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arXiv · 2610.23650

JAXtari: High-Throughput and Easy-to-Modify Arcade Learning Environment

Quentin Delfosse·Raban Emunds·Paul Seitz·Sebastian Wette·Jannis Blüml·Daniel Kirn·Dominik Mandok·Kristian Kersting
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Abstract

The Arcade Learning Environment (ALE) has been a cornerstone of Deep Reinforcement Learning research, providing a standardized benchmark for evaluating general agents. However, its lack of native support for object-extraction and environment modification restricts research into evaluating whether agents learn robust causal models or rely on brittle visual heuristics. Furthermore, the ALE's dependence on CPU-based emulation creates a significant bottleneck in modern GPU-accelerated training pipelines, limiting both experimental scale and iteration frequency. In this work, we introduce JAXtari, an open-source, high-performance reimplementation of Atari 2600 environments. By leveraging massive GPU parallelization, JAXtari allows PPO agents to finish training on 100M environment steps on a single A100-GPU in under an hour. Beyond speed, it provides object-centric observations and a flexible modification system, allowing researchers to, e.g. develop neuro-symbolic methods or discover novel shortcuts and subsequently train and adapt their agents rapidly, sometimes in only minutes.

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Quentin Delfosse, Raban Emunds, Paul Seitz, Sebastian Wette, Jannis Blüml, Daniel Kirn, Dominik Mandok, Kristian Kersting. 2026-10-16. JAXtari: High-Throughput and Easy-to-Modify Arcade Learning Environment. https://arxiv.org/abs/2610.23650

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