  Maps are a very important component of strategy games, and a time-consuming
task if done by hand. Maps generated by traditional PCG techniques such as
Perlin noise or tile-based PCG techniques look unnatural and unappealing, thus
not providing the best user experience for the players. However it is possible
to have a generator that can create realistic and natural images of maps, given
that it is trained how to do so. We propose a model for the generation of maps
based on Generative Adversarial Networks (GAN). In our implementation we tested
out different variants of GAN-based networks on a dataset of heightmaps. We
conducted extensive empirical evaluation to determine the advantages and
properties of each approach. The results obtained are promising, showing that
it is indeed possible to generate realistic looking maps using this type of
approach.
