Lectures for Reinforcement Learning Practical, University of Groningen, Semester Ib.
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Coordinator: Matthia Sabatelli
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Lecturers: Matthia Sabatelli (m.sabatelli@rug.nl) and Nicole Orzan (n.orzan@rug.nl)
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Teaching Assistant: Henry Maathuis (h.maathuis@rug.nl)
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Period: Semester Ib
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When: Monday 9:00-11:00 AM (theoretical lectures) and Monday 15:00-17:00 (Computer Lab)
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Classroom: TBD
| Date | Topic |
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| November 15 | Lecture 1: Foundations of Reinforcement Learning (Matthia Sabatelli): slides |
| November 22 | Lecture 2: Exploration and Bandit Problems (Nicole Orzan): slides |
| November 29 | Lecture 3: Dynamic Programming (Nicole Orzan): slides |
| December 6 | Lecture 4: Model-Free Reinforcement Learning (Matthia Sabatelli): slides |
| December 13 | Lecture 5: Function Approximators (Matthia Sabatelli): slides |
| December 20 | Lecture 6: Beyond Model-Free Reinforcement Learning (Matthia Sabatelli): slides |
| January 10 | Lecture 7: What it means to do research in Reinforcement Learning (Matthia Sabatelli & Nicole Orzan): slides |
| January 17 | Student Presentations 1 |
| January 19 | Student Presentations 2 |
Students should handle in the following three deliverables:
- Assignment 1: one coding assignment related to lecture 2. pdf
- Assignment 2: one mathematical assignment related to lectures 3 and 4. pdf
- Final Project: a reinforcement learning project of the student's choice
Students are allowed to work alone or in groups of a maximum of two people.
There is no exam for this course, the final grade is based on the aforementioned deliverables.
- Assignment 1: counts for 25% of the grade
- Assignment 2: counts for 25% of the grade
- Assignment 3: counts for 50% of the grade