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Emergence of linguistic conventions in multi-agent reinforcement learning
Dorota Lipowska1, Adam Lipowski2
1Faculty of Modern Languages and Literature, Adam Mickiewicz University, Poznań, Poland.
Plos One
|November 30, 2018
Summary
This study models the emergence of signaling conventions using multi-agent reinforcement learning. Population renewal in dense networks enables efficient language formation, avoiding local consensus traps.
Area of Science:
- Computational Linguistics
- Artificial Intelligence
- Game Theory
Background:
- Emergence of signaling conventions, like language, is a complex, interdisciplinary problem.
- Existing research uses diverse assumptions and methodologies, lacking a unifying explanation.
Purpose of the Study:
- To examine the formation of signaling conventions within a multi-agent reinforcement learning (MARL) framework.
- To investigate how network structure and population dynamics influence the emergence of consensus in signaling systems.
Main Methods:
- Utilized a MARL model to simulate agents forming signaling conventions.
- Analyzed agent interaction networks, including complete graphs, dense random graphs, and finite-dimensional lattices.
- Introduced population renewal mechanisms to observe their effect on consensus formation.
Main Results:
- Global consensus and efficient signaling (unique object-word mappings) emerged in complete or dense random graphs.
- Finite-dimensional lattices led to disordered configurations and only local consensus.
- Population renewal, coupled with superlinear reinforcement, overcame local traps and enhanced efficient signaling.
Conclusions:
- Network topology significantly impacts the formation of global signaling conventions.
- Population renewal is crucial for achieving efficient and unified signaling systems in structured environments.
- MARL provides a viable framework for understanding the evolution of communication.
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