# Learning to cooperate: Emergent communication in multi-agent navigation

arXiv: https://arxiv.org/abs/2004.01097 · HTML: https://mlsanity.com/paper/2004.01097/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

## Built on by (most cited first, 18 of 18)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents](https://mlsanity.com/paper/2110.05769/) (2021)
- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [SocraSynth: Multi-LLM Reasoning with Conditional Statistics](https://mlsanity.com/paper/2402.06634/) (2024)
- [Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability](https://mlsanity.com/paper/2106.03982/) (2021)
- [MA-Dreamer: Coordination and communication through shared imagination](https://mlsanity.com/paper/2204.04687/) (2022)
- [Over-communicate no more: Situated RL agents learn concise communication protocols](https://mlsanity.com/paper/2211.01480/) (2022)
- [Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel](https://mlsanity.com/paper/2111.02827/) (2021)
- [Emergent Communication in Interactive Sketch Question Answering](https://mlsanity.com/paper/2310.15597/) (2023)
- [LLM-Mediated Guidance of MARL Systems](https://mlsanity.com/paper/2503.13553/) (2025)
- [A framework for the emergence and analysis of language in social learning agents](https://mlsanity.com/paper/2305.02632/) (2023)
- [Joining the Conversation: Towards Language Acquisition for Ad Hoc Team Play](https://mlsanity.com/paper/2305.12235/) (2023)
- [Detecting danger in gridworlds using Gromov's Link Condition](https://mlsanity.com/paper/2201.06274/) (2022)
- [Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction](https://mlsanity.com/paper/2306.11336/) (2023)
- [From Grunts to Lexicons: Emergent Language from Cooperative Foraging](https://mlsanity.com/paper/2505.12872/) (2025)
- [Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language](https://mlsanity.com/paper/2107.09356/) (2021)
- [A Cooperative Reinforcement Learning Environment for Detecting and Penalizing Betrayal](https://mlsanity.com/paper/2210.12841/) (2022)
- [Robust and Efficient Communication for Multi-Agent Learning](https://mlsanity.com/paper/2609.15361/) (2026)

## Abstract (arXiv)

Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper left room". Using populations of agents, we show that the emergent protocol has basic compositional structure, thus exhibiting a core property of natural language.