Professorship for Reinforcement Learning and Computational Decision-Making

The professorship for Reinforcement Learning and Computational Decision-Making deals with the research and development of novel methodologies for obtaining effective strategies to solve Decision-Making problems and control tasks in simulation and in the real world. We strive for making Reinforcement Learning more efficient and practical, in order to fully express its potential on challenging and impactful real-world problems. In particular, we investigate lightweight methods by addressing open questions in transfer, curriculum, and multi-task learning, where we aim at obtaining strategies that generalise across different problems and seamlessly adapt to previously unseen ones. We conduct our research under a both theoretical and empirical lens, where the rigorous theoretical treatment of the proposed methodologies is complemented with empirical evidence of their effectiveness on simulated benchmarks and real-world applications, e.g., finance, games, robotics.
The Chair will benefit from the theoretical developments of the Chair of Theory of Machine Learning, from the study of the deep connections between Reinforcement Learning and Cognitive Science with the Center for Computational and Theoretical Biology, from the expertise of the Chair of Human-Robot Interaction for robotics applications, and from collaborations with the Department of Economics for applications in finance and recommender systems.