# Signaling and Social Learning in Swarms of Robots

arXiv: https://arxiv.org/abs/2411.11616 · HTML: https://mlsanity.com/paper/2411.11616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 170 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.04402/) (2021)
- [Hearing the shape of an arena with spectral swarm robotics](https://mlsanity.com/paper/2403.17147/) (2024)

## Abstract (arXiv)

This paper investigates the role of communication in improving coordination within robot swarms, focusing on a paradigm where learning and execution occur simultaneously in a decentralized manner. We highlight the role communication can play in addressing the credit assignment problem (individual contribution to the overall performance), and how it can be influenced by it. We propose a taxonomy of existing and future works on communication, focusing on information selection and physical abstraction as principal axes for classification: from low-level lossless compression with raw signal extraction and processing to high-level lossy compression with structured communication models. The paper reviews current research from evolutionary robotics, multi-agent (deep) reinforcement learning, language models, and biophysics models to outline the challenges and opportunities of communication in a collective of robots that continuously learn from one another through local message exchanges, illustrating a form of social learning.