Illuminating Diverse Neural Cellular Automata for Level Generation

• We present a method of generating a collection 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.
• This could be an interesting scenario generator

GPT Agents

• Started on importer

Book

• Send out emails to agents!

SBIRs

• Got all the stories done. Need to assign points, etc.
• 1:00 Sprint planning meeting
• Decided to try to put everything into a TKinter app. I already know the framework pretty well, I just need to brush up. This way I’ll be able to reuse a lot of the GraphNavigator code
• Maybe this?

• Today’s progress: