Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder

Understand

Several works have demonstrated the use of variational autoencoders (VAEs) for generating levels in the style of existing games and blending levels across different games.

• Further, quality-diversity (QD) algorithms have also become popular for generating varied game content by using evolution to explore a search space while focusing on both variety and quality.
• To reap the benefits of both these approaches, we present a level generation and game blending approach that combines the use of VAEs and QD algorithms.
• Specifically, we train VAEs on game levels and run the MAP-Elites QD algorithm using the learned latent space of the VAE as the search space.