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

Aniruddha Srinivas Joshi

2025

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.

Paper Citation

in Harvard Style

Joshi A. (2025). Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP; ISBN 978-989-758-728-3, SciTePress, pages 385-397. DOI: 10.5220/0013373200003912

in Bibtex Style

@conference{grapp25,
author={Aniruddha Joshi},
title={Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP},
year={2025},
pages={385-397},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013373200003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP
TI - Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences
SN - 978-989-758-728-3
AU - Joshi A.
PY - 2025
SP - 385
EP - 397
DO - 10.5220/0013373200003912
PB - SciTePress