Reinforcement Learning for Adaptive Hybrid Intelligence
Reinforcement learning (RL) is one of the main paradigms of machine learning, in which artificial agents learn optimal behaviour from interaction data. Recent years have seen notable breakthroughs in this field in robotics, games such as Atari and Go, and in ChatGPT. While the focus on autonomous and active learning makes reinforcement learning a powerful tool, deployment of a reinforcement learning agent as an assistant or collaborator (that is, as a hybrid intelligence) raises specific challenges.
In this two-day SIKS/HI course we will take a look at the foundations of reinforcement learning and various extensions that are important for hybrid intelligence.
This course is coordinated by Prof. Rineke Verbrugge (RUG), Dr. Erman Acar (UvA) and Dr. Herke van Hoof (UvA).
