Online Game Level Generation from Music

TL;DR

This paper introduces OPARL, a reinforcement learning framework for real-time, music-driven level generation in games that adapts to player speed and music energy, ensuring cohesive and playable content.

Contribution

It presents a novel online level generation method from music using reinforcement learning, integrating local search and k-nearest neighbors for better control and adaptation.

Findings

OPARL effectively generates playable levels matching music energy.

The framework adapts to different player speeds and styles.

Simulation results demonstrate the approach's competence in real-time level creation.

Abstract

Game consists of multiple types of content, while the harmony of different content types play an essential role in game design. However, most works on procedural content generation consider only one type of content at a time. In this paper, we propose and formulate online level generation from music, in a way of matching a level feature to a music feature in real-time, while adapting to players' play speed. A generic framework named online player-adaptive procedural content generation via reinforcement learning, OPARL for short, is built upon the experience-driven reinforcement learning and controllable reinforcement learning, to enable online level generation from music. Furthermore, a novel control policy based on local search and k-nearest neighbours is proposed and integrated into OPARL to control the level generator considering the play data collected online. Results of simulation-based experiments show that our implementation of OPARL is competent to generate playable levels with difficulty degree matched to the ``energy'' dynamic of music for different artificial players in an online fashion.