Learning to Control Active Matter

TL;DR

This paper demonstrates how reinforcement learning can be used to design control protocols for active matter systems, enabling directed transport by manipulating local activity patterns in simulated Vicsek-like particles.

Contribution

It introduces a reinforcement learning approach to control active matter, revealing interpretable protocols for inducing directed transport in non-equilibrium systems.

Findings

Reinforcement learning successfully induces net transport in active matter.

Learned protocols exploit physics of different coupling regimes.

Control patterns are physically interpretable.

Abstract

The study of active matter has revealed novel non-equilibrium collective behaviors, illustrating their potential as a new materials platform. However, most works treat active matter as unregulated systems with uniform microscopic energy input, which we refer to as activity. In contrast, functionality in biological materials results from regulating and controlling activity locally over space and time, as has only recently become experimentally possible for engineered active matter. Designing functionality requires navigation of the high dimensional space of spatio-temporal activity patterns, but brute force approaches are unlikely to be successful without system-specific intuition. Here, we apply reinforcement learning to the task of inducing net transport in a specific direction for a simulated system of Vicsek-like self-propelled disks using a spotlight that increases activity locally. The resulting time-varying patterns of activity learned exploit the distinct physics of the strong and weak coupling regimes. Our work shows how reinforcement learning can reveal physically interpretable protocols for controlling collective behavior in non-equilibrium systems.