Emergence of grounded compositional language in multi-agent populations
Captured source
source ↗Emergence of grounded compositional language in multi-agent populations | OpenAI
March 15, 2017
Publication
Emergence of grounded compositional language in multi-agent populations
Read paper
Loading…
Share
Abstract
By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations. Towards this end, we propose a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language. This language is represented as streams of abstract discrete symbols uttered by agents over time, but nonetheless has a coherent structure that possesses a defined vocabulary and syntax. We also observe emergence of non-verbal communication such as pointing and guiding when language communication is unavailable.
- Learning Paradigms
- Multi-agent
Authors
Igor Mordatch, Pieter Abbeel
Related articles
View all
Scaling laws for reward model overoptimizationPublicationOct 19, 2022
Learning to play Minecraft with Video PreTrainingConclusionJun 23, 2022
Dota 2 with large scale deep reinforcement learningPublicationDec 13, 2019