# Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks

arXiv: https://arxiv.org/abs/2309.06021 · HTML: https://mlsanity.com/paper/2309.06021/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018)
- [Multi-Agent Cooperation and the Emergence of (Natural) Language](https://mlsanity.com/paper/1612.07182/) (2016)
- [Emergent Multi-Agent Communication in the Deep Learning Era](https://mlsanity.com/paper/2006.02419/) (2020)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Learning to Schedule Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1902.01554/) (2019)
- [On the Pitfalls of Measuring Emergent Communication](https://mlsanity.com/paper/1903.05168/) (2019)
- [Anti-efficient encoding in emergent communication](https://mlsanity.com/paper/1905.12561/) (2019)
- [Translating Neuralese](https://mlsanity.com/paper/1704.06960/) (2017)
- [Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theory](https://mlsanity.com/paper/2207.01843/) (2022)

## Built on by (most cited first, 9 of 9)

- [Multi-Agent Reinforcement Learning with Communication-Constrained Priors](https://mlsanity.com/paper/2512.03528/) (2025)
- [Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems](https://mlsanity.com/paper/2604.06914/) (2026)
- [SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking](https://mlsanity.com/paper/2605.05861/) (2026)
- [Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms](https://mlsanity.com/paper/2501.06454/) (2025)
- [Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Delayed Observation](https://mlsanity.com/paper/2505.03586/) (2025)
- [Learning Value of Information towards Joint Communication and Control in 6G V2X](https://mlsanity.com/paper/2505.06978/) (2025)
- [AutoMaAS: Self-Evolving Multi-Agent Architecture Search for Large Language Models](https://mlsanity.com/paper/2510.02669/) (2025)
- [3D-Agent:Tri-Modal Multi-Agent Collaboration for Scalable 3D Object Annotation](https://mlsanity.com/paper/2601.04404/) (2026)
- [FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs](https://mlsanity.com/paper/2608.03852/) (2026)

## Abstract (arXiv)

In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless networks mandate exchanging high dimensional data in dynamic and uncertain environments, therefore implementing communication control tasks becomes challenging and highly complex. Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observable states in a cooperative fashion where agents build an emergent communication protocol to solve complex tasks. This paper articulates the importance of EC-MARL within the context of future 6G wireless networks, which imbues autonomous decision-making capabilities into network entities to solve complex tasks such as autonomous driving, robot navigation, flying base stations network planning, and smart city applications. An overview of EC-MARL algorithms and their design criteria are provided while presenting use cases and research opportunities on this emerging topic.