| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2020W |
| Ziele |
(*)Consolidation of content taught in the lecture through practical implementation
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| Lehrinhalte |
(*)- Implementing and testing selected solution methods for k-armed Bandits
- Implementing and testing selected table-based solution methods for MDPs with discrete state spaces
- Implementing and testing selected approximate solution methods for MDPs with continuous state spaces
|
| Beurteilungskriterien |
(*)- Active participation
- Positive completion of exercises
|
| Lehrmethoden |
(*)- Thorough explanation of the used software
- Verbal explanation of each new exercise, additional hints, programming tips, and practical examples
|
| Abhaltungssprache |
Englisch |
| Literatur |
(*)Richard S. Sutton and Andrew G. Barto. 2018. Introduction to Reinforcement Learning (2nd. edition). MIT Press, Cambridge, MA, USA.
|
| Lehrinhalte wechselnd? |
Nein |
| Äquivalenzen |
(*)in collaboration with 536MLPEREIV20: VL Reinforcement Learning (3 ECTS) equivalent to
536MLPEREIK19: KV Reinforcement Learning (4.5 ECTS)
|
