# Interpretable Learned Emergent Communication for Human-Agent Teams

arXiv: https://arxiv.org/abs/2201.07452 · HTML: https://mlsanity.com/paper/2201.07452/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 53 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 27 of 27)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [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)
- [Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks](https://mlsanity.com/paper/1812.09755/) (2018)
- [Learning to Schedule Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1902.01554/) (2019)
- [Learning Efficient Multi-agent Communication: An Information Bottleneck Approach](https://mlsanity.com/paper/1911.06992/) (2019)
- [Human few-shot learning of compositional instructions](https://mlsanity.com/paper/1901.04587/) (2019)
- [Learning Transferable Cooperative Behavior in Multi-Agent Teams](https://mlsanity.com/paper/1906.01202/) (2019)
- [Transparency and Explanation in Deep Reinforcement Learning Neural Networks](https://mlsanity.com/paper/1809.06061/) (2018)
- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)
- [Learning Agent Communication under Limited Bandwidth by Message Pruning](https://mlsanity.com/paper/1912.05304/) (2019)
- [A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models](https://mlsanity.com/paper/1804.02969/) (2018)
- [Explaining Reinforcement Learning to Mere Mortals: An Empirical Study](https://mlsanity.com/paper/1903.09708/) (2019)
- [Emergent Discrete Communication in Semantic Spaces](https://mlsanity.com/paper/2108.01828/) (2021)
- [Translating Neuralese](https://mlsanity.com/paper/1704.06960/) (2017)
- [The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget](https://mlsanity.com/paper/2004.11935/) (2020)
- [Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning](https://mlsanity.com/paper/2102.12550/) (2021)
- [Community Regularization of Visually-Grounded Dialog](https://mlsanity.com/paper/1808.04359/) (2018)
- [Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.08482/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication](https://mlsanity.com/paper/2409.17348/) (2024)
- [Prime Agent: A Self-Improving RLM Harness](https://mlsanity.com/paper/2608.23552/) (2026)
- [Towards True Lossless Sparse Communication in Multi-Agent Systems](https://mlsanity.com/paper/2212.00115/) (2022)
- [On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.14276/) (2023)
- [Natural, Artificial, and Human Intelligences](https://mlsanity.com/paper/2506.02183/) (2025)

## Abstract (arXiv)

Learning interpretable communication is essential for multi-agent and human-agent teams (HATs). In multi-agent reinforcement learning for partially-observable environments, agents may convey information to others via learned communication, allowing the team to complete its task. Inspired by human languages, recent works study discrete (using only a finite set of tokens) and sparse (communicating only at some time-steps) communication. However, the utility of such communication in human-agent team experiments has not yet been investigated. In this work, we analyze the efficacy of sparse-discrete methods for producing emergent communication that enables high agent-only and human-agent team performance. We develop agent-only teams that communicate sparsely via our scheme of Enforcers that sufficiently constrain communication to any budget. Our results show no loss or minimal loss of performance in benchmark environments and tasks. In human-agent teams tested in benchmark environments, where agents have been modeled using the Enforcers, we find that a prototype-based method produces meaningful discrete tokens that enable human partners to learn agent communication faster and better than a one-hot baseline. Additional HAT experiments show that an appropriate sparsity level lowers the cognitive load of humans when communicating with teams of agents and leads to superior team performance.