Learning to Communicate with Strangers via Channel Randomisation Methods
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Abstract (arXiv)
We introduce two methods for improving the performance of agents meeting for the first time to accomplish a communicative task. The methods are: (1) `message mutation' during the generation of the communication protocol; and (2) random permutations of the communication channel. These proposals are tested using a simple two-player game involving a `teacher' who generates a communication protocol and sends a message, and a `student' who interprets the message. After training multiple agents via self-play we analyse the performance of these agents when they are matched with a stranger, i.e. their zero-shot communication performance. We find that both message mutation and channel permutation positively influence performance, and we discuss their effects.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 19 Apr 2021.
Builds on
14 of 28 references.
References · 14
- 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
- 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
- 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
- 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 257
- 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 206
- 2020"Other-Play" for Zero-Shot Coordinationcited by 194
- 2019Active Domain Randomizationcited by 161
- 2017Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbolscited by 123
- 2018Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Inputcited by 102
- 2019On the Pitfalls of Measuring Emergent Communicationcited by 66
- 2019Anti-efficient encoding in emergent communicationcited by 59
2 more
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2024A Review of the Applications of Deep Learning-Based Emergent Communicationcited by 17
- 2026The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMscited by 10
- 2022Meta-Referential Games to Learn Compositional Learning Behaviourscited by 2
- 2024Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisitioncited by 1
- 2026The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs12 Feb 2026
- 2024A Review of the Applications of Deep Learning-Based Emergent Communication3 Jul 2024
- 2024Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition26 Feb 2024
- 2022Meta-Referential Games to Learn Compositional Learning Behaviours16 Jul 2022