Searching the Latent Space of a Generative Adversarial Network to Generate DOOM Levels

Edoardo Giacomello, Pier Luca Lanzi, Daniele Loiacono

Abstract: In this work, following the same approach successfully applied to evolve Super Mario levels, we applied the CMA-ES to search the latent space of a GAN previously trained to generate DOOM levels. Combining a search algorithm with a model trained in a supervised setting, allows to take advantage from both these paradigms. From one hand, the GAN is able to generate contents exploiting the design patterns learned from all the examples it was trained from. On the other hand, the CMA-ES can effectively search this design space for specific contents that meet some given design objectives. In particular, we tested our approach evolving three very different type of levels: an arena level (i.e., few large areas), a labyrinth level (i.e., many corridors and small areas), and a complex level (i.e., a balanced mix of large and small areas). Our results show that the latent space of a GAN can be effectively searched by the CMA-ES to find DOOM levels that fit accurately the objectives but, at the same time, are also novel.