智能體在數千款3D電子遊戲中進行對抗

Agents Play Thousands of 3D Video Games

March 17, 2025

作者: Zhongwen Xu, Xianliang Wang, Siyi Li, Tao Yu, Liang Wang, Qiang Fu, Wei Yang

cs.AI

摘要

我們提出了PORTAL，這是一個新穎的框架，旨在開發能夠通過語言引導策略生成來玩數千款3D視頻遊戲的人工智慧代理。通過將決策問題轉化為語言建模任務，我們的方法利用大型語言模型（LLMs）生成以領域特定語言（DSL）表示的行為樹。這種方法消除了傳統強化學習方法所帶來的計算負擔，同時保留了戰略深度和快速適應性。我們的框架引入了一種混合策略結構，將基於規則的節點與神經網絡組件相結合，實現了高層次的戰略推理和精確的低層次控制。一個包含定量遊戲指標和視覺語言模型分析的雙重反饋機制，促進了在戰術和戰略層面上的迭代策略改進。由此產生的策略可以即時部署，易於人類理解，並且能夠在多樣化的遊戲環境中進行泛化。實驗結果展示了PORTAL在數千款第一人稱射擊遊戲（FPS）中的有效性，與傳統方法相比，在開發效率、策略泛化和行為多樣性方面均有顯著提升。PORTAL代表了遊戲AI開發的重大進步，為創建能夠在數千款商業視頻遊戲中以最小開發開銷運行的複雜代理提供了實用解決方案。有關3D視頻遊戲的實驗結果，請訪問https://zhongwen.one/projects/portal 查看最佳效果。

English

We present PORTAL, a novel framework for developing artificial intelligence agents capable of playing thousands of 3D video games through language-guided policy generation. By transforming decision-making problems into language modeling tasks, our approach leverages large language models (LLMs) to generate behavior trees represented in domain-specific language (DSL). This method eliminates the computational burden associated with traditional reinforcement learning approaches while preserving strategic depth and rapid adaptability. Our framework introduces a hybrid policy structure that combines rule-based nodes with neural network components, enabling both high-level strategic reasoning and precise low-level control. A dual-feedback mechanism incorporating quantitative game metrics and vision-language model analysis facilitates iterative policy improvement at both tactical and strategic levels. The resulting policies are instantaneously deployable, human-interpretable, and capable of generalizing across diverse gaming environments. Experimental results demonstrate PORTAL's effectiveness across thousands of first-person shooter (FPS) games, showcasing significant improvements in development efficiency, policy generalization, and behavior diversity compared to traditional approaches. PORTAL represents a significant advancement in game AI development, offering a practical solution for creating sophisticated agents that can operate across thousands of commercial video games with minimal development overhead. Experiment results on the 3D video games are best viewed on https://zhongwen.one/projects/portal .