Reinforcement Learning

Master Computer Science, Leiden University, The Netherlands

Welcome to the webpage of the master course 'Reinforcement Learning' taught at Leiden University

Welcome to the master course "Reinforcement Learning", which will run in the spring of 2023. The course consists of 14 weeks, in which you hand in 3 assignments, and make a final exam. Reinforcement Learning is an interesting topic -- how can we make computers learn like animals and humans -- and we try to make this course as much fun as well. The course teaches RL theory, but also has a strong practical focus, with several graded assignments about hands-on deep reinforcement learning.

Instructors: Aske Plaat, Thomas Moerland, Matthias Muller-Brockhausen, Zhao Yang, Jonathan Collu, Riccardo Majellaro.

Lectures: Fridays, 9.00-11.00, Gorlaeus C3.

Practicals: Fridays, 11.00-13.00, Snellius 3.02.

Important information

  • In-person education: The course is taught in person. Reinforcement Learning is NOT an online course!

  • Brightspace: We use Brightspace for all communication throughout the course: announcements, delivering slides, handing-out & handing in assignments, providing grades, content questions (forum), etc.

  • Attend the lectures: You should attend all lectures if you want to pass the course. The lecture slides are on Brightspace. Do not come if you are sick. Ask a friend for their lecture notes. Or re-read the chapter in the book, go over the slides, and ask questions when you have recovered.

  • Assignments & practicals: The assignments are the core learning experience of the course. You are encouraged to work on the assignments during the practical and ask the TAs your questions.

  • Read the book: In order to pass this course, you must also read the book (see Literature) -- lectures only is not enough. Read the chapter before you come to the lecture. Ask questions. The teachers are friendly -- they have been specially selected -- and love answering questions.

  • Prior knowledge: The course is about DEEP Reinforcement Learning. We assume that you are familiar with classical machine learning and with deep learning. We provide a special refresher summary lecture in the first practical slot, Friday 10 Feb 2023 11:00 in C3. Just stay after the first lecture and see if it is useful. Appendix B of the book also provides an introduction.