Reinforcement Learning

Course type: Lecture + Exercise
Time: Lecture: Friday 14:00 - 15:30
Location: Building 106 SR 00-007 (Friday)
Organizers: Frank Hutter, Marius Lindauer, Joschka Bödecker, Gabriel Kalweit
Web page: , ILIAS

Reinforcement Learning


  • All news and exercise sheets will be online at ILAS


The lecture deals with methods of Reinforcement Learning that constitute an important class of machine learning algorithms. Starting with the formalization of problems as Markov decision processes, a variety of Reinforcement Learning methods are introduced and discussed in-depth. The connection to practice-oriented problems is established throughout the lecture based on many examples.


__The course will be in English.__ The course will be held in a flipped classroom manner. The students have to watch a lecture video each week at home and we will meet weekly to answer questions, discuss the new content and start to solve exercises. Roughly every week, there will be a new exercise sheet. Most exercises will be practical (teams of 2-3 students!) so that you learn how to apply reinforcement learning in practice. You must get 50% points from the exercise to participate in the final exam (see below).
  • Building: 106; Room: SR 00 007
  • Session: Friday 14:00 - 15:30


In the end, everyone has to implement a larger project which is the base of the final oral exam. In the first 15 minutes of the oral exam, you have to present your project and in the second 15 minutes, we will ask you to answer questions about further course material.