Collective foraging of active particles trained by reinforcement learning.

ABSTRACT: Collective self-organization of animal groups is a recurring phenomenon in nature which has attracted a lot of attention in natural and social sciences. To understand how collective motion can be achieved without the presence of an external control, social interactions have been considered which regulate the motion and orientation of neighbors relative to each other. Here, we want to understand the motivation and possible reasons behind the emergence of such interaction rules using an experimental model system of light-responsive active colloidal particles (APs). Via reinforcement learning (RL), the motion of particles is optimized regarding their foraging behavior in presence of randomly appearing food sources. Although RL maximizes the rewards of single APs, we observe the emergence of c

SUBMITTER: Loffler RC 

PROVIDER: S-EPMC10564893 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature