# Biases for Emergent Communication in Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1912.05676 · HTML: https://mlsanity.com/paper/1912.05676/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 30 of 48)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Learning to Ground Multi-Agent Communication with Autoencoders](https://mlsanity.com/paper/2110.15349/) (2021)
- [Emergent Discrete Communication in Semantic Spaces](https://mlsanity.com/paper/2108.01828/) (2021)
- [Quasi-Equivalence Discovery for Zero-Shot Emergent Communication](https://mlsanity.com/paper/2103.08067/) (2021)
- [Emergent Communication under Competition](https://mlsanity.com/paper/2101.10276/) (2021)
- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [Emergent Graphical Conventions in a Visual Communication Game](https://mlsanity.com/paper/2111.14210/) (2021)
- [Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models](https://mlsanity.com/paper/2205.12392/) (2022)
- [Towards More Human-like AI Communication: A Review of Emergent Communication Research](https://mlsanity.com/paper/2308.02541/) (2023)
- [The Collaboration Gap: Exploration and Benchmarking of Open-World Agentic Cooperation](https://mlsanity.com/paper/2511.02687/) (2025)
- [Learning Multi-Agent Communication with Contrastive Learning](https://mlsanity.com/paper/2307.01403/) (2023)
- [Echoing: Identity Failures when LLM Agents Talk to Each Other](https://mlsanity.com/paper/2511.09710/) (2025)
- [First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization](https://mlsanity.com/paper/2205.12381/) (2022)
- [Towards Human-Agent Communication via the Information Bottleneck Principle](https://mlsanity.com/paper/2207.00088/) (2022)
- [Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation](https://mlsanity.com/paper/2302.09368/) (2023)
- [Multi-Agent Curricula and Emergent Implicit Signaling](https://mlsanity.com/paper/2106.11156/) (2021)
- [Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation](https://mlsanity.com/paper/2305.05116/) (2023)
- [MARL with General Utilities via Decentralized Shadow Reward Actor-Critic](https://mlsanity.com/paper/2106.00543/) (2021)
- [Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level](https://mlsanity.com/paper/2110.13621/) (2021)
- [AI Mother Tongue: Self-Emergent Communication in MARL via Endogenous Symbol Systems](https://mlsanity.com/paper/2507.10566/) (2025)
- [Learning to Ground Decentralized Multi-Agent Communication with Contrastive Learning](https://mlsanity.com/paper/2203.03344/) (2022)
- [Over-communicate no more: Situated RL agents learn concise communication protocols](https://mlsanity.com/paper/2211.01480/) (2022)
- [Towards True Lossless Sparse Communication in Multi-Agent Systems](https://mlsanity.com/paper/2212.00115/) (2022)
- [Learning Multi-Object Positional Relationships via Emergent Communication](https://mlsanity.com/paper/2302.08084/) (2023)
- [Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel](https://mlsanity.com/paper/2111.02827/) (2021)
- [On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.14276/) (2023)
- [Heterogeneous Adversarial Play in Interactive Environments](https://mlsanity.com/paper/2510.18407/) (2025)
- [Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word](https://mlsanity.com/paper/2205.15027/) (2022)
- [Recommendations for Systematic Research on Emergent Language](https://mlsanity.com/paper/2206.11302/) (2022)
- [Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments](https://mlsanity.com/paper/2209.10958/) (2022)

## Abstract (arXiv)

We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks. In temporally extended reinforcement learning domains, it has proved hard to learn such communication without centralized training of agents, due in part to a difficult joint exploration problem. We introduce inductive biases for positive signalling and positive listening, which ease this problem. In a simple one-step environment, we demonstrate how these biases ease the learning problem. We also apply our methods to a more extended environment, showing that agents with these inductive biases achieve better performance, and analyse the resulting communication protocols.