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Poster
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Jakob Foerster · Ioannis Assael · Nando de Freitas · Shimon Whiteson

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #37 #None
in Monday Posters »

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains.

Keywords: Deep Learning or Neural Networks · Reinforcement Learning Algorithms

Author Information

Jakob Foerster (University of Oxford)

Jakob Foerster is a PhD student in AI at the University of Oxford under the supervision of Shimon Whiteson and Nando de Freitas. Using deep reinforcement learning he studies the emergence of communication in multi-agent AI systems. Prior to his PhD Jakob spent four years working at Google and Goldman Sachs. Previously he has also worked on a number of research projects in systems neuroscience, including work at MIT and the Weizmann Institute.

Yannis Assael (University of Oxford)
Nando de Freitas (University of Oxford)
Shimon Whiteson (University of Oxford)

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