Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network

Generative Adversarial Networks (GANs) are a machine learning
approach capable of generating novel example outputs across a
space of provided training examples. Procedural Content Generation
(PCG) of levels for video games could benefit from such models,
especially for games where there is a pre-existing corpus of levels
to emulate. This paper trains a GAN to generate levels for Super
Mario Bros using a level from the Video Game Level Corpus. The
approach successfully generates a variety of levels similar to one
in the original corpus, but is further improved by application of
the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).
Specifically, various fitness functions are used to discover levels
within the latent space of the GAN that maximize desired properties.
Simple static properties are optimized, such as a given distribution
of tile types. Additionally, the champion A* agent from the 2009
Mario AI competition is used to assess whether a level is playable,
and how many jumping actions are required to beat it. These fitness
functions allow for the discovery of levels that exist within the
space of examples designed by experts, and also guide the search
towards levels that fulfill one or more specified objectives.