NitroGen：面向通用遊戲代理的開放基礎模型

摘要

我們推出NitroGen——這款專為通用遊戲智能體設計的視覺行動基礎模型，其訓練數據涵蓋逾千款遊戲、總計四萬小時的遊戲影片。該模型融合三大核心要素：1）透過自動化擷取公開遊戲影片中的玩家操作，建構出網路級規模的影片行動數據集；2）具備跨遊戲泛化能力評估功能的多遊戲基準環境；3）採用大規模行為克隆訓練的統一視覺行動模型。NitroGen在多元遊戲場景中展現卓越能力，包括3D動作遊戲的戰鬥遭遇、2D平台遊戲的高精度操控，以及程序生成世界的探索任務。該模型能有效遷移至未見過的遊戲，相較於從零開始訓練的模型，任務成功率最高可提升52%。我們公開數據集、評估套件與模型權重，以推動通用具身智能體的研究進展。

English

We introduce NitroGen, a vision-action foundation model for generalist gaming agents that is trained on 40,000 hours of gameplay videos across more than 1,000 games. We incorporate three key ingredients: 1) an internet-scale video-action dataset constructed by automatically extracting player actions from publicly available gameplay videos, 2) a multi-game benchmark environment that can measure cross-game generalization, and 3) a unified vision-action model trained with large-scale behavior cloning. NitroGen exhibits strong competence across diverse domains, including combat encounters in 3D action games, high-precision control in 2D platformers, and exploration in procedurally generated worlds. It transfers effectively to unseen games, achieving up to 52% relative improvement in task success rates over models trained from scratch. We release the dataset, evaluation suite, and model weights to advance research on generalist embodied agents.

NitroGen：面向通用遊戲代理的開放基礎模型

NitroGen: An Open Foundation Model for Generalist Gaming Agents

摘要

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