Emergent aggregation from collective foraging

2026-08-28

AI Summary

研究通过强化学习觅食者在仅感知同类的情况下优化个体寻找可再生目标的行为，揭示了间接资源驱动奖励作为集体现象出现的通用路径。

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.

Problem

• collective behaviour
• emergent aggregation
• reinforcement learning
• foraging
• indirect reward

Innovation

• reinforcement learning foragers
• indirect objective
• emergent collective phenomena
• scale-agnostic collective search