Report
Learning to cooperate: Emergent communication in multi-agent navigation
| العنوان: | Learning to cooperate: Emergent communication in multi-agent navigation |
|---|---|
| المؤلفون: | Kajić, Ivana, Aygün, Eser, Precup, Doina |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Multiagent Systems, Statistics - Machine Learning |
| الوصف: | Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper left room". Using populations of agents, we show that the emergent protocol has basic compositional structure, thus exhibiting a core property of natural language. Comment: Accepted to CogSci 2020 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2004.01097 |
| رقم الانضمام: | edsarx.2004.01097 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |