标题： 通过赫布学习的涌现异构群体控制 显示英文标题

标题： Emergent Heterogeneous Swarm Control Through Hebbian Learning

摘要： 在本文中，我们引入赫布学习作为群体机器人学的一种新方法，使异质性自动出现。 赫布学习呈现一种基于生物启发的神经适应形式，仅依赖于局部信息。 通过这样做，我们解决了学习异质控制的几个主要挑战：1）赫布学习通过局部学习规则消除了将涌现现象归因于单个代理的复杂性，从而绕过了微观-宏观问题；2）所有群体成员统一的赫布学习规则限制了所需参数的数量，随着群体规模的扩大缓解了维度灾难；以及3）基于群体行为进化的赫布学习规则减少了通常用于优化异质群体所需的大量先验知识。 这项工作表明，通过赫布学习，异质性自然出现，导致群体层面的行为切换，并显著提高了群体能力。 它还展示了赫布学习规则的进化如何可以作为标准基准任务中多智能体强化学习的有效替代方案。 显示英文摘要

摘要： In this paper, we introduce Hebbian learning as a novel method for swarm robotics, enabling the automatic emergence of heterogeneity. Hebbian learning presents a biologically inspired form of neural adaptation that solely relies on local information. By doing so, we resolve several major challenges for learning heterogeneous control: 1) Hebbian learning removes the complexity of attributing emergent phenomena to single agents through local learning rules, thus circumventing the micro-macro problem; 2) uniform Hebbian learning rules across all swarm members limit the number of parameters needed, mitigating the curse of dimensionality with scaling swarm sizes; and 3) evolving Hebbian learning rules based on swarm-level behaviour minimises the need for extensive prior knowledge typically required for optimising heterogeneous swarms. This work demonstrates that with Hebbian learning heterogeneity naturally emerges, resulting in swarm-level behavioural switching and in significantly improved swarm capabilities. It also demonstrates how the evolution of Hebbian learning rules can be a valid alternative to Multi Agent Reinforcement Learning in standard benchmarking tasks.