| Detailed information |
| Original study plan |
Bachelor's programme Artificial Intelligence 2025W |
| Learning Outcomes |
Competences |
| See lecture series (Vorlesung) by the same name.
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| Skills |
Knowledge |
| See lecture series (Vorlesung) by the same name.
|
See lecture series (Vorlesung) by the same name.
|
|
| Criteria for evaluation |
Positive completion of exercises. Active participation in discussions.
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| Methods |
Students solve simple exercises, including implementation and experimentation with simple reinforcement learning algorithms on toy problems. Thorough explanation of the used software. Verbal explanation of each new exercise, additional hints, programming tips, and practical examples.
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| Language |
English |
| Study material |
Richard S. Sutton and Andrew G. Barto. 2018. Introduction to Reinforcement Learning (2nd. edition). MIT Press, Cambridge, MA, USA.
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| Changing subject? |
No |
| Further information |
This exercise course (UE) and the corresponding lecture series course (VO) form a didactic unit. The study results described here are achieved through the combination of these two courses.
|
| Corresponding lecture |
in collaboration with 536MLPEREIV20: VL Reinforcement Learning (3 ECTS) equivalent to
536MLPEREIK19: KV Reinforcement Learning (4.5 ECTS)
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