Abstract
The study of active matter has revealed novel non-equilibrium collective behaviors, illustrating their potential as a new materials platform. However, most work 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.
2 More- Received 11 May 2021
- Accepted 25 August 2021
DOI:https://doi.org/10.1103/PhysRevResearch.3.033291
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Published by the American Physical Society