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Reinforcement Learning

Reinforcement Learning (Lecture)


  • Organized by:
    • Prof. Joschka Boedecker and Gabriel Kalweit
  • Lecture:
    • HISinOne number: 11LE13V-1141
  • Language:
    • English
  • Session:
    • Friday 14:00 - 16:00
    • Building 082 HS 00 006
  • Exam:
    • TBA



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 held in a flipped classroom manner. We'll watch a lecture recording at home each week and meet to answer questions, discuss the new content and the exercises. The exercises will not be mandatory. However, there will be a project for which you have to get 50% of the points in order to participate in the final exam.


All lecture recordings, slides and exercises can be found in ILIAS.