Policy heterogeneity improves collective olfactory search in three-dimensional turbulence.

Abstract

We examine how heterogeneous swarms, mixing exploratory and exploitative agents with distinct decision rules, consistently outperform homogeneous ones where each agent balances exploration and exploitation individually, performing experiments in two contrasting deployment scenarios. Using odor fields from state-of-the-art direct numerical simulations of the 3D Navier-Stokes equations, we find that policy diversity typically allows the group to reach the source of the odor more efficiently by mitigating the detrimental effects of spatiotemporal turbulent correlations. These findings provide insights into collective search behavior and offer promising strategies for the design of robust, bioinspired search algorithms in engineered systems.