# Emergence of Communication in an Interactive World with Consistent Speakers

arXiv: https://arxiv.org/abs/1809.00549 · HTML: https://mlsanity.com/paper/1809.00549/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2018.

## 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)
- [Learning to Ground Multi-Agent Communication with Autoencoders](https://mlsanity.com/paper/2110.15349/) (2021)
- [Multi-agent Reinforcement Learning: A Comprehensive Survey](https://mlsanity.com/paper/2312.10256/) (2023)
- [Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents](https://mlsanity.com/paper/2110.05769/) (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)
- [Towards More Human-like AI Communication: A Review of Emergent Communication Research](https://mlsanity.com/paper/2308.02541/) (2023)
- [Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication](https://mlsanity.com/paper/2111.06464/) (2021)
- [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026)
- [Visual Referential Games Further the Emergence of Disentangled Representations](https://mlsanity.com/paper/2304.14511/) (2023)
- [Learning Multi-Object Positional Relationships via Emergent Communication](https://mlsanity.com/paper/2302.08084/) (2023)
- [Meta-Referential Games to Learn Compositional Learning Behaviours](https://mlsanity.com/paper/2207.08012/) (2022)
- [Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima](https://mlsanity.com/paper/2303.04544/) (2023)
- [Implicit Repair with Reinforcement Learning in Emergent Communication](https://mlsanity.com/paper/2502.12624/) (2025)
- [On the Correspondence between Compositionality and Imitation in Emergent Neural Communication](https://mlsanity.com/paper/2305.12941/) (2023)
- [In Pursuit of Predictive Models of Human Preferences Toward AI Teammates](https://mlsanity.com/paper/2503.15516/) (2025)
- [CtD: Composition through Decomposition in Emergent Communication](https://mlsanity.com/paper/2601.10169/) (2026)
- [ETHER: Aligning Emergent Communication for Hindsight Experience Replay](https://mlsanity.com/paper/2307.15494/) (2023)

## Abstract (arXiv)

Training agents to communicate with one another given task-based supervision only has attracted considerable attention recently, due to the growing interest in developing models for human-agent interaction. Prior work on the topic focused on simple environments, where training using policy gradient was feasible despite the non-stationarity of the agents during training. In this paper, we present a more challenging environment for testing the emergence of communication from raw pixels, where training using policy gradient fails. We propose a new model and training algorithm, that utilizes the structure of a learned representation space to produce more consistent speakers at the initial phases of training, which stabilizes learning. We empirically show that our algorithm substantially improves performance compared to policy gradient. We also propose a new alignment-based metric for measuring context-independence in emerged communication and find our method increases context-independence compared to policy gradient and other competitive baselines.