  Dynamical systems models for controlling multi-agent swarms have demonstrated
advances toward resilient, decentralized navigation algorithms. We previously
introduced the NeuroSwarms controller, in which agent-based interactions were
modeled by analogy to neuronal network interactions, including attractor
dynamics and phase synchrony, that have been theorized to operate within
hippocampal place-cell circuits in navigating rodents. This complexity
precludes linear analyses of stability, controllability, and performance
typically used to study conventional swarm models. Further, tuning dynamical
controllers by hand or grid search is often inadequate due to the complexity of
objectives, dimensionality of model parameters, and computational costs of
simulation-based sampling. Here, we present a framework for tuning dynamical
controller models of autonomous multi-agent systems based on Bayesian
Optimization (BayesOpt). Our approach utilizes a task-dependent objective
function to train Gaussian Processes (GPs) as surrogate models to achieve
adaptive and efficient exploration of a dynamical controller model's parameter
space. We demonstrate this approach by studying an objective function selecting
for NeuroSwarms behaviors that cooperatively localize and capture spatially
distributed rewards under time pressure. We generalized task performance across
environments by combining scores for simulations in distinct geometries. To
validate search performance, we compared high-dimensional clustering for high-
vs. low-likelihood parameter points by visualizing sample trajectories in
Uniform Manifold Approximation and Projection (UMAP) embeddings. Our findings
show that adaptive, sample-efficient evaluation of the self-organizing
behavioral capacities of complex systems, including dynamical swarm
controllers, can accelerate the translation of neuroscientific theory to
applied domains.
