Arbitrarily Scalable Environment Generators via Neural Cellular Automata

TL;DR

This paper introduces a neural cellular automata-based approach to generate arbitrarily large, scalable environments for multi-robot systems, overcoming previous size limitations and enabling efficient large-scale simulation and policy transfer.

Contribution

It proposes training neural cellular automata environment generators with quality diversity algorithms to produce large, consistent environments from small training samples.

Findings

NCA generators produce consistent, regularized large environments.

Method scales to thousands of robots in simulation.

Single-agent policies transfer effectively to large environments.

Abstract

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}.