The Fun Facets of Mario: Multifaceted Experience-Driven PCG via Reinforcement Learning

Wang，Ziqi1; Liu，Jialin1; Yannakakis，Georgios N.2

10.1145/3555858.3563282

2022-09-05

ACM International Conference Proceeding Series

The recently introduced EDRL framework approaches the experience-driven (ED) procedural generation of game content via a reinforcement learning (RL) perspective. EDRL has so far shown its effectiveness in generating novel platformer game levels endlessly in an online fashion. This paper extends the framework by integrating multiple facets of game creativity in the ED generation process. In particular, we employ EDRL on the creative facets of game level and gameplay design in Super Mario Bros. Inspired by Koster's theory of fun, we formulate fun as moderate degrees of level or gameplay divergence and equip the algorithm with such reward functions. Moreover, we enable faster and more efficient game content generation through an episodic generative soft actor-critic algorithm. The resulting multifaceted EDRL is not only capable of generating fun levels efficiently, but it is also robust with respect to dissimilar playing styles and initial game level conditions.

Experience-driven procedural content generation online level generation platformer games procedural content generation via reinforcement learning Super Mario Bros

First

English

2-s2.0-85142381764

Scopus

1.Southern University of Science and Technology,China
2.Institute of Digital Games,University of Malta,Malta

Wang，Ziqi,Liu，Jialin,Yannakakis，Georgios N.. The Fun Facets of Mario: Multifaceted Experience-Driven PCG via Reinforcement Learning[C],2022.