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

TL;DR

This paper introduces a reinforcement learning-enhanced procedural generation framework for AR environments, enabling dynamic, contextually coherent maps that adapt to gameplay and enhance immersive narrative experiences.

Contribution

The paper develops a novel RL-integrated WFC framework that adapts map generation to dynamic AR scenarios, improving quality and responsiveness over static methods.

Findings

Superior map quality demonstrated in evaluations

Enhanced user immersion in AR experiences

Potential applications in education and training

Abstract

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.