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.

翻译：程序化内容生成（PCG）在游戏开发中被广泛用于创建可扩展且多样化的环境。然而，现有方法（如波函数坍缩算法）通常局限于静态场景，缺乏动态叙事驱动应用（尤其在增强现实游戏中）所需的适应性。本文提出一种面向移动增强现实环境的强化学习增强型波函数坍缩框架。该方法通过整合环境特定规则以及基于强化学习的动态图块权重调整，生成既满足上下文连贯性又能响应游戏需求的地图。对比评估与用户研究表明，该框架实现了更优的地图质量并提供了沉浸式体验，特别适用于叙事驱动的增强现实游戏。此外，该方法在需要动态自适应环境的教育、模拟训练和沉浸式扩展现实等领域具有广阔的应用前景。