Automatically generating 3D games in commercial game engines remains a non-trivial challenge, as it involves complex engine-related workflows for generating assets such as scenes, blueprints, and code. To address this challenge, we propose a novel multi-agent system, AutoUE, which coordinates multiple agents to end-to-end generate 3D games, covering model retrieval, scene generation, gameplay and interaction code synthesis, and automated game testing for evaluation. In order to mitigate tool-use hallucinations in LLMs, we introduce a retrieval-augmented generation mechanism that grounds agents with relevant UE tool documentation. Additionally, we incorporate game design patterns and engine constraints into the code generation process to ensure the generation of correct and robust code. Furthermore, we design an automated play-testing pipeline that generates and executes runtime test commands, enabling systematic evaluation of dynamic behaviors. Finally, we construct a game generation dataset and conduct a series of experiments that demonstrate AutoUE's ability to generate 3D games end-to-end, and validate the effectiveness of these designs.

翻译：在商业游戏引擎中自动生成3D游戏仍然是一项具有挑战性的任务，因为这涉及生成场景、蓝图和代码等资产的复杂引擎相关工作流。为应对这一挑战，我们提出了一种新颖的多智能体系统AutoUE，该系统通过协调多个智能体端到端地生成3D游戏，涵盖模型检索、场景生成、玩法与交互代码合成以及用于评估的自动化游戏测试。为缓解大语言模型在使用工具时产生的幻觉问题，我们引入了检索增强生成机制，使智能体能够基于相关的虚幻引擎工具文档进行决策。此外，我们将游戏设计模式和引擎约束融入代码生成过程，以确保生成正确且健壮的代码。进一步地，我们设计了一个自动化游戏测试流程，该流程能够生成并执行运行时测试指令，从而实现对动态行为的系统性评估。最后，我们构建了一个游戏生成数据集并进行了一系列实验，验证了AutoUE端到端生成3D游戏的能力，并证明了上述设计的有效性。