AI 中文总结
该研究发现，强化学习觅食者仅通过个体觅食奖励即可涌现出空间聚集行为，无需直接的分组奖励，为理解涌现集体现象提供了资源驱动的通用途径。
AI 中文摘要
生物系统中的集体行为通常被建模为直接社会驱动的结果：智能体被赋予奖励或硬连线，以与邻居对齐或接近邻居。在此，我们证明聚集可以反过来从间接目标中涌现。我们让强化学习觅食者（初始执行随机游走）从寻找可补充目标的纯粹个体奖励中优化其动态，且仅感知同种个体，永远不感知目标本身。随着视觉范围增大，智能体经历从环境调整的个体搜索到尺度无关的集体搜索的急剧转变，且该转变与空间聚集的开始同时发生。因此，集体阶段作为最优觅食的副产品出现，无需任何分组的直接奖励。一个最小的分析首通模型将该转变再现为两种搜索策略之间的交叉。我们的结果表明，间接的、资源驱动的奖励是涌现集体现象的通用途径。
英文摘要
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.
Comments11 pages, 8 figures
