  We present a method of generating diverse collections of neural cellular
automata (NCA) to design video game levels. While NCAs have so far only been
trained via supervised learning, we present a quality diversity (QD) approach
to generating a collection of NCA level generators. By framing the problem as a
QD problem, our approach can train diverse level generators, whose output
levels vary based on aesthetic or functional criteria. To efficiently generate
NCAs, we train generators via Covariance Matrix Adaptation MAP-Elites (CMA-ME),
a quality diversity algorithm which specializes in continuous search spaces. We
apply our new method to generate level generators for several 2D tile-based
games: a maze game, Sokoban, and Zelda. Our results show that CMA-ME can
generate small NCAs that are diverse yet capable, often satisfying complex
solvability criteria for deterministic agents. We compare against a
Compositional Pattern-Producing Network (CPPN) baseline trained to produce
diverse collections of generators and show that the NCA representation yields a
better exploration of level-space.
