Learning to Communicate with Deep Multi-Agent Reinforcement Learning - Jakob Foerster

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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, when no prior communication protocol is provided.

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.