English(EN) JAXtari: High-Throughput and Easy-to-Modify Arcade Learning Environment

JAXtari 通过 GPU 加速的 Atari 环境加速深度强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

一个名为 JAXtari 的街机学习环境的新开源实现已被开发出来，以解决深度强化学习研究中的性能和修改限制。JAXtari 利用 GPU 并行化显著加快了训练速度，使智能体能够在不到一小时的时间内，在单个 A100 GPU 上完成数百万次环境交互。除了速度，它还提供面向对象的观测和灵活的修改系统，允许研究人员探索新颖的方法并更快地适应智能体。 AI

影响加速深度强化学习智能体的迭代周期，从而能够更快地试验新算法和环境修改。

排序理由介绍人工智能研究新软件环境的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Quentin Delfosse, Raban Emunds, Paul Seitz, Sebastian Wette, Jannis Bl\"uml, Daniel Kirn, Dominik Mandok, Kristian Kersting · 2026-10-19 04:00

JAXtari：高吞吐量且易于修改的街机学习环境

arXiv:2610.23650v1 Announce Type: new 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 en…

本文如何被排名

报道来源 [1]

JAXtari：高吞吐量且易于修改的街机学习环境

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