Online Game Level Generation from Music

Wang，Ziqi1,2; Liu，Jialin1,2

10.1109/CoG51982.2022.9893695

2022

2325-4270

2325-4289

978-1-6654-5990-7

IEEE Conference on Computatonal Intelligence and Games, CIG

2022-August

119-126

21-24 Aug. 2022

Beijing, China

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.

EDPCG EDRL online level generation player-adaptive Procedural content generation

第一 ; 通讯

英语

National Natural Science Foundation of China[61906083];Shenzhen Fundamental Research Program[JCYJ20190809121403553];

2-s2.0-85139088150

Scopus

1.Research Institute of Trustworthy Autonomous System,Southern University of Science and Technology (SUSTech),Shenzhen,China
2.Southern University of Science and Technology (SUSTech),Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China

Wang，Ziqi,Liu，Jialin. Online Game Level Generation from Music[C],2022:119-126.