Learning to Ground Multi-Agent Communication with Autoencoders

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Lin, Toru

Advisor dc:contributor.advisor

• Isola, Phillip J.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/140012

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/140012