Mathematical Machine Learning -- Reinforcement Learning

Course Information

The information on this website is preliminary and will be updated continuously. Additional information will soon be available publicly in the LSF information system of the university.

Description #

This seminar is addressed to master students of Scientific Computing and Mathematics. Together we will learn about reinforcement learning, from the very basics until more advances concepts like policy gradient methods.

Topics #

In the following, an excerpt of the list of contents of “Reinforcement Learning - An Introduction” by Sutton and Barto are listed:

Chapters 1 & 2 should be read in preparation before the start of the seminar. Chapters 3 through 12 (excluding 11) will be presentation topics, one for each of the 9 participating students.

Exam / Presentations #

Participant will prepare and present a presentation on one of the sections of “Reinforcement Learning - An Introduction” by Sutton and Barto, as listed in Topics. The presentations are prepared and presented alone, i.e., no group work, and should be approximately one hour long. Grading is based on the presentation and the discussion thereafter. If you have questions, you can contact us.

Dates and Timeline #

The organizational meeting was on Tuesday 16.04.2024 at 6pm in SR 10. During this meeting, participants chose one of the available topics.

Starting KW20 (20th week of the year), we will meet regularly once a week for each presentation. In the organizational meeting we decided on a regular meeting time. The regular meetings will be on Tuesdays 18:00 in SR 10.

(Pre-)registration #

The registration is closed, the participants are fixed, and have an assigned presentation topic.

Resources #

• Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.

  • available at incompleteideas

• Slides of the organizational meeting here