标题： 增强强化学习的程序生成用于动态叙事驱动的增强现实体验 显示英文标题

标题： Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences

摘要： 程序内容生成（PCG）被广泛用于在游戏中创建可扩展和多样化环境。 然而，现有的方法，如波函数坍缩（WFC）算法，通常局限于静态场景，并缺乏动态、叙事驱动应用所需的适应性，特别是在增强现实（AR）游戏中。 本文提出了一种增强强化学习的WFC框架，专为移动AR环境设计。 通过整合特定于环境的规则和由强化学习（RL）提供的动态瓷砖权重调整，所提出的方法生成既上下文连贯又对游戏需求有响应的地图。 比较评估和用户研究显示，该框架实现了优越的地图质量和沉浸式体验，使其非常适合叙事驱动的AR游戏。 此外，该方法在教育、模拟训练和沉浸式扩展现实（XR）体验等更广泛的应用中也具有前景，这些领域需要动态和自适应环境。 显示英文摘要

摘要： 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.