title: Agents Play Thousands of 3D Video Games
summary: 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 .
paper_authors._id: 67dcfafa73e582f950d360f3
paper_authors.name: Zhongwen Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-21T11:40:17.259Z
paper_authors._id: 672db76fa34d64e774fc42c9
paper_authors.avatarUrl: /avatars/be529f4bfcc40514697facbe8e874735.svg
paper_authors.fullname: Zhongwen Xu
paper_authors.type: user
paper_authors.user: zhongwenxu
paper_authors._id: 67dcfafa73e582f950d360f4
paper_authors.name: Xianliang Wang
paper_authors._id: 67dcfafa73e582f950d360f5
paper_authors.name: Siyi Li
paper_authors._id: 67dcfafa73e582f950d360f6
paper_authors.name: Tao Yu
paper_authors._id: 67dcfafa73e582f950d360f7
paper_authors.name: Liang Wang
paper_authors._id: 67dcfafa73e582f950d360f8
paper_authors.name: Qiang Fu
paper_authors._id: 67dcfafa73e582f950d360f9
paper_authors.name: Wei Yang
paper_title: Agents Play Thousands of 3D Video Games
paper_summary: 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 .
paper_ai_summary: A framework that uses large language models to generate behavior trees for AI agents to play 3D video games, combining rule-based and neural network components with a dual-feedback mechanism.
paper_ai_keywords: large language models behavior trees domain-specific language hybrid policy structure dual-feedback mechanism vision-language model first-person shooter game AI development behavior diversity