  We study the problem of generating arbitrarily large environments to improve
the throughput of multi-robot systems. Prior work proposes Quality Diversity
(QD) algorithms as an effective method for optimizing the environments of
automated warehouses. However, these approaches optimize only relatively small
environments, falling short when it comes to replicating real-world warehouse
sizes. The challenge arises from the exponential increase in the search space
as the environment size increases. Additionally, the previous methods have only
been tested with up to 350 robots in simulations, while practical warehouses
could host thousands of robots. In this paper, instead of optimizing
environments, we propose to optimize Neural Cellular Automata (NCA) environment
generators via QD algorithms. We train a collection of NCA generators with QD
algorithms in small environments and then generate arbitrarily large
environments from the generators at test time. We show that NCA environment
generators maintain consistent, regularized patterns regardless of environment
size, significantly enhancing the scalability of multi-robot systems in two
different domains with up to 2,350 robots. Additionally, we demonstrate that
our method scales a single-agent reinforcement learning policy to arbitrarily
large environments with similar patterns. We include the source code at
\url{https://github.com/lunjohnzhang/warehouse_env_gen_nca_public}.
