| Detailed information |
| Original study plan |
Bachelor's programme Artificial Intelligence 2020W |
| Objectives |
Consolidation of content taught in the lecture through practical implementation
|
| Subject |
- 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
|
| Criteria for evaluation |
- Active participation
- Positive completion of exercises
|
| Methods |
- Thorough explanation of the used software
- Verbal explanation of each new exercise, additional hints, programming tips, and practical examples
|
| Language |
English |
| Study material |
Richard S. Sutton and Andrew G. Barto. 2018. Introduction to Reinforcement Learning (2nd. edition). MIT Press, Cambridge, MA, USA.
|
| Changing subject? |
No |
| Corresponding lecture |
in collaboration with 536MLPEREIV20: VL Reinforcement Learning (3 ECTS) equivalent to
536MLPEREIK19: KV Reinforcement Learning (4.5 ECTS)
|
