Learning to school in the presence of hydrodynamic interactions

TL;DR

This paper models fish schooling as vortex dipoles influenced by hydrodynamics, demonstrating that adaptive decision-making enables stable formations and effort minimization, highlighting the importance of flow interactions in collective behavior.

Contribution

It introduces a reinforcement learning approach for adaptive decision-making in hydrodynamically interacting swimmers, advancing understanding of flow-mediated schooling mechanisms.

Findings

Adaptive swimmers maintain formation by adjusting their gaits.

Flow interactions influence the stability of schooling patterns.

Effort-efficient schooling configurations are identified through optimization.

Abstract

Schooling, an archetype of collective behavior, emerges from the interactions of fish responding to visual and other informative cues mediated by their aqueous environment. In this context, a fundamental and largely unexplored question concerns the role of hydrodynamics. Here, we investigate schooling by modeling swimmers as vortex dipoles whose interactions are governed by the Biot-Savart law. When we enhance these dipoles with behavioral rules from classical agent based models we find that they do not lead robustly to schooling due to flow mediated interactions. In turn, we present dipole swimmers equipped with adaptive decision-making that learn, through a reinforcement learning algorithm, to adjust their gaits in response to non-linearly varying hydrodynamic loads. The dipoles maintain their relative position within a formation by adapting their strength and school in a variety of prescribed geometrical arrangements. Furthermore, we identify schooling patterns that minimize the individual and the collective swimming effort, through an evolutionary optimization. The present work suggests that the adaptive response of individual swimmers to flow-mediated interactions is critical in fish schooling.