# Emergent Heterogeneous Swarm Control Through Hebbian Learning

arXiv: https://arxiv.org/abs/2507.11566 · HTML: https://mlsanity.com/paper/2507.11566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 55 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics](https://mlsanity.com/paper/1910.01465/) (2019)
- [Meta-Learning through Hebbian Plasticity in Random Networks](https://mlsanity.com/paper/2007.02686/) (2020)
- [Heterogeneous Multi-Robot Reinforcement Learning](https://mlsanity.com/paper/2301.07137/) (2023)
- [Learning Vision-based Flight in Drone Swarms by Imitation](https://mlsanity.com/paper/1908.02999/) (2019)
- [Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework](https://mlsanity.com/paper/2203.05285/) (2022)
- [Lighthouse Positioning System: Dataset, Accuracy, and Precision for UAV Research](https://mlsanity.com/paper/2104.11523/) (2021)
- [Individuality in Swarm Robots with the Case Study of Kilobots: Noise, Bug, or Feature?](https://mlsanity.com/paper/2305.16063/) (2023)
- [Leveraging Uncertainty in Collective Opinion Dynamics with Heterogeneity](https://mlsanity.com/paper/2402.03354/) (2024)
- [Environment induced emergence of collective behaviour in evolving swarms with limited sensing](https://mlsanity.com/paper/2203.11585/) (2022)
- [Automatically designing robot swarms in environments populated by other robots: an experiment in robot shepherding](https://mlsanity.com/paper/2404.18221/) (2024)
- [Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots](https://mlsanity.com/paper/2503.12406/) (2025)

## Abstract (arXiv)

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.