Information about the course

Thank you for joining in the course Reinforcement Learning!

The course starts in week 17, and there will be a Welcome Zoom Session on April 20 at 16:15. The detailed schedule can be accessed under syllabus page under the course or using the calendar function on the general left menu.
Note 1: The lectures and some other moments (e.g., start of homework assignments) are also included in the calendar, but these dates are merely suggestions. You should complete them offline, at your own pace.
Note 2: The only strict times are the assignment deadlines (including peer-reviews and group assignments) and the joint class sessions over Zoom.

The course page on Studium has been updated with all the information for the first weeks of the course.

Course rules and structure

Please read carefully the course structure and rules here: Course Rules and Structure
There, you will find the overall organization of the course, as well as the examination rules. Please pay special attention to the rules concerning the homework assignments, and the study groups.

Study groups

The class has been randomly distributed over 7 study groups, each study group has its own sub-page on Studium. The groups are meant to be used for offline study, to discuss the lectures and tinkering notebooks. Each group must also contribute with insights and discussion points to each of the Class Discussion Sessions.

It is up to you how each group is organised. You are free to use the collaboration tools within Studium, meet over Zoom, work on a shared Google Docs, etc. See what best fits you. Also, take this as a chance to see the undergraduate student's perspective of online teaching!

Navigating Studium

To navigate the course in Studium, we recommend that you check these links:

Syllabus

Modules

Assignments

You will also find your study group for the course here: People

Study Material

The course uses the video lectures from David Silver’s course on Reinforcement Learning. David Silver is a part of AlphaGo’s team which had the famous successes in Go against world champions. Links to the slides and lecture videos are available here: General Study Instructions.

We will use Reinforcement Learning: An introduction, 2nd edition by Sutton and Barto. It is available as a free pdf.

We have provide detailed reading instructions for each lecture relating the video lectures to the book, as well as Jupyter Notebooks for you to practice with some of the concepts and algorithms. You will find these within the different pages of each Module.

Other material

If you need a refresher for some basic probability concepts such as expectation, conditional expectation, definition of Markov chain, you can use this source.