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Signaling and Social Learning in Swarms of Robots

arXiv 2411.11616 ↗First posted 18 Nov 2024cs.RO cs.AI cs.LGcited by 0

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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.

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.

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References · 2

  1. 2021Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learningcited by 56
  2. 2024Hearing the shape of an arena with spectral swarm roboticscited by 2

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  1. 2023Simulation of robot swarms for learning communication-aware coordination
  2. 2026The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs
  3. 2025From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms
  4. 2026Modular Reinforcement Learning For Cooperative Swarms
  5. 2026Emergence, Not Bandwidth: Physical Coupling and the Limits of Learned Multi-Agent Communication
  6. 2026Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization
  7. 2026Communication Methods in Multi-Agent Reinforcement Learning
  8. 2026Robust and Efficient Communication for Multi-Agent Learning