| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2019W |
| Ziele |
(*)Introduction to basic principles and techniques of Reinforcement Learning.
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| Lehrinhalte |
(*)Defining the Reinforcement Learning problem relative to supervised and unsupervised machine learning. Defining k-armed Bandits, contextual bandits, and finally the Markov Decision Process (MDP). Formalizing the RL problem. Basic table-based algorithms to explore the space of possible solution methods. Introduction of more useful, advanced, but approximate solution methods. Examples of state-of-the-art methods, including deep Reinforcement Learning.
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| Beurteilungskriterien |
(*)Multiple programming exercises in Python throughout the semester. Written exam at the end of the semester.
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| Lehrmethoden |
(*)Slide presentation, discussion of common problems during the exercise.
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)Slides used in the lecture will be made available via Moodle.
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| Lehrinhalte wechselnd? |
Nein |
| Sonstige Informationen |
(*)R. Sutton and A. Barto (2018). Reinforcement Learning: An Introduction. (Second Edition). Cambridge, MA: The MIT Press.
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