TDDE78 · 6 credits · Advanced level (A1N) · Informationsteknologi, Datateknik, Datavetenskap
Reinforcement Learning
Active · next start VT 2027
94%
passed the latest regular sitting
LAB1 · 12 Jun 2026 · 16 students
94%
passed the regular sittings, all sittings over the last 5 years
LAB1 · 16 students
Part of 3 programmes
Hand-in or lab
LAB1 Laboratory work
- 5
- 4
- 3
- U
Main rounds
94% passed · 16 students
- 94%16
Later rounds
No sittings with at least 5 students.
- Get 4 or 5: 88% (16 students)
About the course
Aim
The course gives an introduction to the field reinforcement learning. The aim is that students should be acquainted with different methods that are used for learning based on feedback.
On completion of the course, you should be able to:
- identify basic concepts, terminology, theories, models and methods in reinforcement learning
- develop and systematically test basic methods in reinforcement learning
- evaluate different reinforcement learning algorithms experimentally as well as interpret and document results of experimental studies
- have a broad knowledge to be able to read and profit by literature in the area
Content
Foundational course in reinforcement learning, mainly from a computer science perspective. The focus is on introducing the main concepts, techniques, and methods including
- Value-based, policy-based, and model-based reinforcement learning methods
- Actor-Critic methods
- Multi-agent reinforcement learning
- Multi-objective reinforcement learning
- Hierarchical reinforcement learning
- Examiner
- Fredrik Heintz
- Scheduled hours
- 0 h
- Main field of study
- Informationsteknologi, Datateknik, Datavetenskap