Advanced Topics in Reinforcement Learning and Planning

Course type


Study programs


Students will present papers from a list of important, recent works in machine learning compiled by the professor. Topics include deep and reinforcement learning, and transformer and GNN architectures. The focus will be on the use of these techniques in simple applications that are crisp and easy to understand. The list of papers will also include those underlying well-known learning systems like ChaptGPT, AlphaZero, Gato, etc. The final list of papers and topics to be covered will be defined after the first meeting.

Bachelor degree in CS or equivalent and basic AI and ML courses, with some previous exposure to deep learning and reinforcement learning.


TBA, see previous semester for reference.


Please register via the SUPRA platform offered by the department of CS.


If you have any questions, please contact Jonas Reiher.