Institut für Grundlagen der Informationsverarbeitung
Assoc. Prof. Dr. Robert Legenstein
Office hours: by appointment (via e-mail)
In this seminar, we will discuss Reinforcement Learning in depth.
Reinforcement Learning is a very important subfield of Machine Learning,
where learning is not performed from explicit target labels, but from
We will start with the basic concepts and algorithms. Our treatment of the topic will be based on a new book PDF.
Talks should be no longer than 35 minutes, and they should be be clear,
interesting and informative, rather than a reprint of the material.
Select what parts of the material you want to present, and what not, and
then present the selected material well (including definitions not given
in the material: look them up on the web or if that is not successful,
ask the seminar organizers). Often diagrams or figures are useful for a
talk. on the other hand, giving in the talk numbers of references that
are listed at the end is a no-no (a talk is an online process, not meant
to be read). For the same reasons you can also quickly repeat earlier
definitions or so if you suspect that the audience may not remember it.
Talks will be assigned at the first seminar meeting. Students are requested to have a quick glance at the topics prior to this meeting in order to determine their preferences. Note that the number of participants for this seminar will be limited. Preference will be given to students who
|Date||#||Topic / paper title||Presenter 1||Presenter 2||Presentation|
|20.3.2018||1||Chapter 2: Multi-armed bandits||Könighofer||PDF
|20.3.2018||2||Chapter 3: Markov Decision Processes||Basirat||Ebrahimi||PDF
|20.3.2018||3||Chapter 4: Dynamic Programming||Karl||PDF
|24.4.2018||4||Chapter 5: Monte Carlo methods||Gigerl||Petschenig||PDF
|24.4.2018||5||Chapter 6: Temporal-Differences Learning||Ahmetovic||Music||PDF
|8.5.2018||6||Chapter 7: n-step Bootstraping||Kassarnig||PDF
|8.5.2018||7||Chapter 8: Planning and Learning with Tabular Methods||Schlacher||Schlüsselbauer
|15.5.2018||8||Chapter 14: Reinforcement Learning and Psychology||Benninger||Hajek
|15.5.2018||9||Chapter 15: Reinforcement Learning and Neuroscience||Raggam||canceled
|29.5.2018||10||Chapter 9: On-policy prediction with approximation||Toth||Zöhrer
|29.5.2018||11||Chapter 10: On-policy control with approximation||Hehenberger||PDF
|12.6.2018||12||Chapter 12: Eligibility traces||Gherman||Moik
|12.6.2018||13||Chapter 13: Policy Gradient Methods||Spataru||PDF
|25.6.2018||15||Learning to play boardgames with Reinforcement||Remonda||PDF