外观
论文arXiv 2410.1751711 天前
蜂群思维是单个强化学习智能体
原文链接：The hive mind is a single reinforcement learning agent（arXiv 2410.17517 · 发布日期：2026-10-17）
The hive mind is a single reinforcement learning agent
论文证明局部模仿规则产生的群体认知等价于单个在线RL智能体，并在蜜蜂摇摆舞加权投票模型中分析。
核心要点
- 研究连接群体模仿决策与单智能体试错学习两种自然策略形成机制。
- 将分布式个体的局部模仿规则表述为从多臂老虎机并行实例聚合样本的在线RL智能体。
- 在蜜蜂摇摆舞的盲目模仿加权投票模型中，推导出Maynard-Cross Learning更新规则。
- 框架可用于分析模仿成功策略的经济与社会系统，并启发可扩展的RL式集体系统设计。
验证范围与局限：分析范围限于摘要所述的理论等价及蜜蜂摇摆舞盲目模仿加权投票模型。
英文摘要 (Abstract)
Abstract Decision-making is an essential attribute of any intelligent agent or group. Natural systems are known to converge to effective strategies through at least two distinct mechanisms: collective decision-making via imitation of others, and trial-and-error by a single agent. This paper establishes an equivalence between these two paradigms. We show that the emergent distributed cognition (sometimes referred to as the hive mind ) arising from individuals following simple, local imitation-based rules is that of a single online reinforcement learning (RL) agent aggregating action-value samples from many parallel instances of a multi-armed bandit. More specifically, we show that, in the blindly imitative weighted voter model of honey bees’ waggle dance, the update rule through which this macro-agent learns is an RL algorithm that we coin Maynard-Cross Learning . Our analysis implies that a group of individuals following simple imitative strategies can be equivalent to a more complex learning entity, substantiating the idea that group-level intelligence may explain how seemingly irrational individual behaviors are selected in nature. Beyond biology, the framework offers new tools for analyzing economic and social systems where individuals imitate successful strategies, effectively participating in a collective learning process. Our findings may further inform the design of scalable RL-inspired collective systems in artificial domains.
作者：Karthik Soma, Yann Bouteiller, Heiko Hamann, Giovanni Beltrame
来源
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