Emergent aggregation from collective foraging
Machine LearningMultiagent Systems
Summary
The authors show that animals or agents can start grouping together even if they are only trying to find food and not trying to be social. They used computer simulations where agents learned to find targets better, only seeing each other but not the targets directly. When the agents could see farther, they switched from searching alone to moving as a group, which helped them find food better by chance. This group behavior emerged naturally as a side effect of trying to find food, without any reward for sticking together. The authors also made a simple math model that explains this change between two searching ways.
Authors
Gorka Muñoz-Gil, Andrea López-Incera, Vide Ramsten, Giovanni Volpe, Thomas Müller, Hans J. Briegel
Abstract
Collective behaviour in living systems is usually modelled as the outcome of a \emph{direct} social drive: agents are rewarded, or hard-wired, to align with or approach their neighbours. Here we show that aggregation can instead emerge from an \emph{indirect} objective. We let reinforcement learning foragers, initially performing a random walk, optimize their dynamics from a purely individual reward for finding replenishable targets, while perceiving only their conspecifics and never the targets themselves. As the visual range grows, the agents undergo a sharp crossover from an environment-tuned individual search to a scale-agnostic collective one, and this crossover coincides with the onset of spatial aggregation. Thus a collective phase arises as a by-product of optimal foraging, without any direct reward for grouping. A minimal analytical first-passage model reproduces the transition as a crossover between the two search strategies. Our results identify indirect, resource-driven reward as a generic route to emergent collective phenomena.