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School of Science and Technology

Reinforcement Learning, 3 Credits

Örebro University offers a course in Reinforcement Learning. The course provides a general introduction both in theory and practice.

Förstärkt inlärning

Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. The course is part of the education initiative Smarter at Örebro University.

The course provides a general introduction to Reinforcement Learning in theory and practice. The following items are covered:

  • Basic concepts of Reinforcement Learning.
  • Formalization of Reinforcement Learning tasks, environments and agents.
  • Basic Reinforcement Learning algorithms for finite systems.
  • Basic Reinforcement Learning algorithms for continuous action spaces.
  • Basics of deep Reinforcement Learning algorithms.
  • Practical aspects of using Reinforcement Learning for control of real-world systems.

This contract education initiative is aimed at working professionals with a higher education qualification of 180 credits earned at the first cycle (bachelor's level) with computer science/computer technology as the main field of study, or alternatively a higher education qualification of 180 credits earned at the first cycle (bachelor's level) in e.g. computer technology/computer science/systems science (which includes computer programming).

In addition, English B/English 6 is required.

The course is free of charge.

Apply to the course here.

The application is only available in Swedish at the moment. Please contact Johan Axelsson if you have any questions.

Coordinator of Smarter and professional education in AI

Johan Axelsson

Title: Contract Education Coordinator School/office: Communication and Collaboration

Profile page: Johan Axelsson


Phone: +46 19 303211

Room: T2201

Johan Axelsson

Course administrator

Jenny Tiberg

Title: Study and Research Administrator School/office: School of Science and Technology

Profile page: Jenny Tiberg


Phone: +46 19 303320

Room: T1109

Jenny Tiberg