用强化学习让游戏地图自动适应剧情和玩家行为。
Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences
- 结合强化学习动态调整瓦片权重，生成更贴合情境的地图。
- 用户测试显示生成地图沉浸感更强，叙事连贯性显著提升。
- 适合需要动态环境的AR/VR游戏、教育模拟等场景。
程序化内容生成（PCG）广泛用于游戏中的可扩展且多样的环境构建。然而，现有方法如波函数坍缩（WFC）算法通常局限于静态场景，难以适应动态、叙事驱动的应用，尤其在增强现实（AR）游戏中表现不足。本文提出一种基于强化学习增强的WFC框架，专为移动AR环境设计。通过融合环境特有规则与由强化学习（RL）驱动的动态瓦片权重调整，该方法生成既上下文一致又响应游戏需求的地图。对比评估与用户研究证实，该框架在地图质量上表现优异，能提供高度沉浸式体验，特别适合叙事驱动型AR游戏。此外，该方法在教育、仿真训练及沉浸式扩展现实（XR）等需动态自适应环境的领域也具有广阔应用前景。
原文摘要 · Abstract (English)
Procedural Content Generation (PCG) is widely used to create scalable and diverse environments in games. However, existing methods, such as the Wave Function Collapse (WFC) algorithm, are often limited to static scenarios and lack the adaptability required for dynamic, narrative-driven applications, particularly in augmented reality (AR) games. This paper presents a reinforcement learning-enhanced WFC framework designed for mobile AR environments. By integrating environment-specific rules and dynamic tile weight adjustments informed by reinforcement learning (RL), the proposed method generates maps that are both contextually coherent and responsive to gameplay needs. Comparative evaluations and user studies demonstrate that the framework achieves superior map quality and delivers immersive experiences, making it well-suited for narrative-driven AR games. Additionally, the method holds promise for broader applications in education, simulation training, and immersive extended reality (XR) experiences, where dynamic and adaptive environments are critical.
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