  Variational autoencoders (VAEs) have been shown to be able to generate game
levels but require manual exploration of the learned latent space to generate
outputs with desired attributes. While conditional VAEs address this by
allowing generation to be conditioned on labels, such labels have to be
provided during training and thus require prior knowledge which may not always
be available. In this paper, we apply Gaussian Mixture VAEs (GMVAEs), a variant
of the VAE which imposes a mixture of Gaussians (GM) on the latent space,
unlike regular VAEs which impose a unimodal Gaussian. This allows GMVAEs to
cluster levels in an unsupervised manner using the components of the GM and
then generate new levels using the learned components. We demonstrate our
approach with levels from Super Mario Bros., Kid Icarus and Mega Man. Our
results show that the learned components discover and cluster level structures
and patterns and can be used to generate levels with desired characteristics.
