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Maps are a very important component of strategy games, and a time-consumingtask if done by hand. Maps generated by traditional PCG techniques such asPerlin noise or tile-based PCG techniques look unnatural and unappealing, thusnot providing the best user experience for the players. However it is possibleto have a generator that can create realistic and natural images of maps, giventhat it is trained how to do so. We propose a model for the generation of mapsbased on Generative Adversarial Networks (GAN). In our implementation we testedout different variants of GAN-based networks on a dataset of heightmaps. Weconducted extensive empirical evaluation to determine the advantages andproperties of each approach. The results obtained are promising, showing thatit is indeed possible to generate realistic looking maps using this type ofapproach.

GAN-Based Content Generation of Maps for Strategy Games