Abstract: In recent years we have seen rapid progress on a number of zero-sum benchmark problems in artificial intelligence, e.g. Go, Poker and Dota. In contrast to these competitive settings, success in the real world typically requires humans, and will require AI agents, to cooperate, communicate and coordinate with others. Crucially, from a learning point of view, these three Cs require fundamentally novel approaches, methods and theory, which has been at the heart of my research agenda. In my talk I will cover recent progress, including how agents can learn to entice others to cooperate in settings of conflicting goals by accounting for their learning behaviour, how they can learn to communicate by reasoning over (public) beliefs and how they can learn policies that can coordinate with other agents at test time by exploiting the symmetries in the environment. I will finish the talk by outlining some of the promising directions for future work.

Bio: Jakob Foerster is a research scientist at Facebook AI Research, incoming assistant professor at the University of Toronto and a Canada CIFAR AI Chair. His work focuses on multi-agent reinforcement learning, emergent communication, human-AI coordination, game theory & planning. He earned his PhD under Shimon Whiteson at the University of Oxford, where he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI and DeepMind. He was the lead organiser of the first Emergent Communication (EmeCom) workshop at NeurIPS in 2017, which he has helped organise ever since.