  Generative Adversarial Networks (GANs) are capable of generating convincing
imitations of elements from a training set, but the distribution of elements in
the training set affects to difficulty of properly training the GAN and the
quality of the outputs it produces. This paper looks at six different GANs
trained on different subsets of data from the game Lode Runner. The quality
diversity algorithm MAP-Elites was used to explore the set of quality levels
that could be produced by each GAN, where quality was defined as being beatable
and having the longest solution path possible. Interestingly, a GAN trained on
only 20 levels generated the largest set of diverse beatable levels while a GAN
trained on 150 levels generated the smallest set of diverse beatable levels,
thus challenging the notion that more is always better when training GANs.
