Reinforcement learning (RL) is a systematic approach to learning and decision making. Developed and studied for decades, recent combinations of RL with modern deep learning have led to impressive demonstrations of the capabilities of today's RL systems, and have fuelled an explosion of interest and research activity. Join this tutorial to learn about the foundations of RL - elegant ideas that give rise to agents that can learn extremely complex behaviors in a wide range of settings. Broadening out, I give a (subjective) overview of where we currently are in terms of what's possible. I conclude with an outlook on key opportunities - both for future research and for real-world applications of RL.
Katja Hofmann (University of Amsterdam)
Dr. Katja Hofmann is a Principal Researcher at the Game Intelligence group at Microsoft Research Cambridge, UK. There, she leads a research team that focuses on reinforcement learning with applications in modern video games. She and her team strongly believe that modern video games will drive a transformation of how we interact with AI technology. One of the projects developed by her team is Project Malmo, which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Katja's long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems. Before joining Microsoft Research, Katja completed her PhD in Computer Science as part of the ILPS group at the University of Amsterdam. She worked with Maarten de Rijke and Shimon Whiteson on interactive machine learning algorithms for search engines.