This lecture is part of the Machine Learning Masters program at the University of Tübingen. The course is run by the Autonomous Learning Group at the MPI for Intelligent Systems.
Mo 16:15 - 17:45: virtual online lecture: zoom link
Tue. 14:15 - 15:45: virtual recitation (starting on Nov 10th) Zoom link in ILIAS
Exam: to be announced
The course will provide you with the theoretical and practical knowledge of reinforcement learning, a field of machine learning concerned with decision making and interaction with dynamical systems, such as robots. We start with a brief overview of supervised learning and spend the most time on reinforcement learning. The exercises will help you get hands-on with the methods and deepen your understanding.
Students gain an understanding of reinforcement learning formulations, problems, and algorithms on a theoretical and practical level. After this course, students should be able to implement and apply deep reinforcement learning algorithms to new problems.
Instructor: Dr. Georg Martius
Teaching Assistants: Sebastian Blaes, Marin Vlastelica, Maximilian Seitzer
Lecture slides and exercises are available on ILIAS!.
- Lecture: Recording
I started recording a bit late. Next time, I hope to have my video also included in the recording,
Background reading: C.M. Bishop Pattern Recognition and Machine Learning, Chap. 5
- Lecture: Recording,
- Lecture: Recording Background reading: Sutton and Barto Reinforcement learning for the next few lectures (for this lecture, parts of Chapter 3)
- Lecture: Recording, Background reading: Sutton and Barto Reinforcement learning Chapter 4
- Lecture: Recording, Background reading: Sutton and Barto Reinforcement learning First part of Chapters 5,6,7, 12
- Sutton & Barto, Reinforcement Learning: An Introduction
- Bertsekas, Dynamic Programming and Optimal Control, Vol. 1
- Bishop, Pattern Recognition and Machine Learning