| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2020W |
| Ziele |
(*)To communicate basic knowledge and core theories relating to the field of reinforcement learning
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| Lehrinhalte |
(*)- Solution methods for k-armed Bandits, and their practical application
- Formal treatment of Markov Decision Problems (MDPs)
- Theory for solving MDPs
- Comprehensive overview of table-based solution methods for MDPs with discrete state spaces
- Selected approximate solution methods for MDPs with continuous state spaces
- Outlook on the current state of the art for the approximate solution of MDPs with very large discrete state spaces and deterministic dynamics (e.g. board games such as „Chess“ and „Go“)
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| Beurteilungskriterien |
(*)- positive grade on the final exam
|
| Lehrmethoden |
(*)- lectures
- positive reinforcement of active lecture participation through rewards in the form of small chocolate treats
|
| Abhaltungssprache |
Englisch |
| Literatur |
(*)Richard S. Sutton and Andrew G. Barto. 2018. Introduction to Reinforcement Learning (2nd. edition). MIT Press, Cambridge, MA, USA.
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| Lehrinhalte wechselnd? |
Nein |
| Äquivalenzen |
(*)in collaboration with 536MLPEREIU20: UE Reinforcement Learning (1.5 ECTS) equivalent to
536MLPEREIK19: KV Reinforcement Learning (4.5 ECTS)
|
