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Poster
Learning Multiagent Communication with Backpropagation
Sainbayar Sukhbaatar · arthur szlam · Rob Fergus

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #147
in Tuesday Posters »

Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

Keywords: Deep Learning or Neural Networks · Reinforcement Learning Algorithms

Author Information

Sainbayar Sukhbaatar (NYU)
arthur szlam (Facebook)
Rob Fergus (New York University)

Rob Fergus is an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University. He received a Masters in Electrical Engineering with Prof. Pietro Perona at Caltech, before completing a PhD with Prof. Andrew Zisserman at the University of Oxford in 2005. Before coming to NYU, he spent two years as a post-doc in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William Freeman. He has received several awards including a CVPR best paper prize, a Sloan Fellowship & NSF Career award and the IEEE Longuet-Higgins prize.

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