  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.
