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Generative Adversarial Networks (GANs) can generate levels for a variety ofgames. This paper focuses on combining GAN-generated segments in a snakingpattern to create levels for Mega Man. Adjacent segments in such levels can beorthogonally adjacent in any direction, meaning that an otherwise fine segmentmight impose a barrier between its neighbor depending on what sorts of segmentsin the training set are being most closely emulated: horizontal, vertical, orcorner segments. To pick appropriate segments, multiple GANs were trained ondifferent types of segments to ensure better flow between segments. Flow wasfurther improved by evolving the latent vectors for the segments being joinedin the level to maximize the length of the level's solution path. Usingmultiple GANs to represent different types of segments results in significantlylonger solution paths than using one GAN for all segment types, and a humansubject study verifies that these levels are more fun and have more human-likedesign than levels produced by one GAN.

Using Multiple Generative Adversarial Networks to Build Better-Connected Levels for Mega Man