| Detailed information |
| Original study plan |
Bachelor's programme Artificial Intelligence 2019W |
| Objectives |
Introduction to basic principles and techniques of Reinforcement Learning.
|
| Subject |
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.
|
| Criteria for evaluation |
Multiple programming exercises in Python throughout the semester. Written exam at the end of the semester.
|
| Methods |
Slide presentation, discussion of common problems during the exercise.
|
| Language |
English |
| Study material |
Slides used in the lecture will be made available via Moodle.
|
| Changing subject? |
No |
| Further information |
R. Sutton and A. Barto (2018). Reinforcement Learning: An Introduction. (Second Edition). Cambridge, MA: The MIT Press.
|
