mlsanityData to 7 Oct 2026

Learning to Communicate with Strangers via Channel Randomisation Methods

arXiv 2104.09557 ↗First posted 19 Apr 2021cs.LG cs.AI cs.CLcited by 4

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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

  1. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  3. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  4. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  5. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 257
  6. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 206
  7. 2020"Other-Play" for Zero-Shot Coordinationcited by 194
  8. 2019Active Domain Randomizationcited by 161
  9. 2017Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbolscited by 123
  10. 2018Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Inputcited by 102
  11. 2019On the Pitfalls of Measuring Emergent Communicationcited by 66
  12. 2019Anti-efficient encoding in emergent communicationcited by 59
2 more
  1. 2018Compositional Obverter Communication Learning From Raw Visual Inputcited by 30
  2. 2020Co-evolution of language and agents in referential gamescited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.

4 papers.