{"ID":2895030,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.11566","arxiv_id":"2507.11566","title":"Emergent Heterogeneous Swarm Control Through Hebbian Learning","abstract":"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.","short_abstract":"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 heterog...","url_abs":"https://arxiv.org/abs/2507.11566","url_pdf":"https://arxiv.org/pdf/2507.11566v1","authors":"[\"Fuda van Diggelen\",\"Tugay Alperen Karagüzel\",\"Andres Garcia Rincon\",\"A. E. Eiben\",\"Dario Floreano\",\"Eliseo Ferrante\"]","published":"2025-07-14T18:59:19Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AI\",\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}