Learning to Ground Multi-Agent Communication with Autoencoders

• URL: http://arxiv.org/abs/2110.15349v1
• Date: Thu, 28 Oct 2021 17:57:26 GMT
• Title: Learning to Ground Multi-Agent Communication with Autoencoders
• Authors: Toru Lin, Minyoung Huh, Chris Stauffer, Ser-Nam Lim, Phillip Isola
• Abstract summary: Communication requires a common language, a lingua franca, between agents. We demonstrate a simple way to ground language in learned representations. We find that a standard representation learning algorithm is sufficient for arriving at a grounded common language.
• Score: 43.22048280036316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Communication requires having a common language, a lingua franca, between agents. This language could emerge via a consensus process, but it may require many generations of trial and error. Alternatively, the lingua franca can be given by the environment, where agents ground their language in representations of the observed world. We demonstrate a simple way to ground language in learned representations, which facilitates decentralized multi-agent communication and coordination. We find that a standard representation learning algorithm -- autoencoding -- is sufficient for arriving at a grounded common language. When agents broadcast these representations, they learn to understand and respond to each other's utterances and achieve surprisingly strong task performance across a variety of multi-agent communication environments.

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