Using Unconditional Diffusion Models in Level Generation for Super Mario Bros

Abstract

This study introduces a novel methodology for generating levels in the iconic video game Super Mario Bros. using a diffusion model based on a UNet architecture. The model is trained on existing levels, represented as a categorical distribution, to accurately capture the game’s fundamental mechanics and design principles. The proposed approach demonstrates notable success in producing high-quality and diverse levels, with a significant proportion being playable by an artificial agent. This research emphasizes the potential of diffusion models as an efficient tool for procedural content generation and highlights their potential impact on the development of new video games and the enhancement of existing games through generated content.