232 research outputs found
Hunting active Brownian particles: Learning optimal behavior
We numerically study active Brownian particles that can respond to
environmental cues through a small set of actions (switching their motility and
turning left or right with respect to some direction) which are motivated by
recent experiments with colloidal self-propelled Janus particles. We employ
reinforcement learning to find optimal mappings between the state of particles
and these actions. Specifically, we first consider a predator-prey situation in
which prey particles try to avoid a predator. Using as reward the squared
distance from the predator, we discuss the merits of three state-action sets
and show that turning away from the predator is the most successful strategy.
We then remove the predator and employ as collective reward the local
concentration of signaling molecules exuded by all particles and show that
aligning with the concentration gradient leads to chemotactic collapse into a
single cluster. Our results illustrate a promising route to obtain local
interaction rules and design collective states in active matter.Comment: to appear in Phys. Rev.
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