Online Game Level Generation from Music
This addresses the challenge of harmonizing multiple content types in procedural content generation for game design, though it appears incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of generating game levels in real-time from music, matching level features to music features while adapting to player speed, and shows that their OPARL framework can generate playable levels with difficulty matched to music energy for different players in online simulations.
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.