arXiv Machine Learning October 19, 2026 By Quentin Delfosse, Raban Emunds, Paul Seitz, Sebastian Wette, Jannis Bl\"uml, Daniel Kirn, Dominik Mandok, Kristian Kersting

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

Read the original on arXiv Machine Learning →

JAXtari is an open‑source, high‑performance reimplementation of Atari 2600 environments that enables massive GPU parallelization. It allows PPO agents to complete 100 M environment steps on a single A100‑GPU in under an hour, far faster than the CPU‑based ALE. Additionally, JAXtari provides object‑centric observations and a flexible modification system, facilitating research into causal modeling, neuro‑symbolic methods, and rapid agent adaptation.

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