Learning to Ground Multi-Agent Communication with Autoencoders
Author(s)
Lin, Toru
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Advisor
Isola, Phillip J.
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Communication requires having a common language, a lingua franca, between agents. This language could emerge via a consensus process between agents, but this 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.
Date issued
2021-09Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology