Reinforcement Learning

The course is an advanced course focusing on reinforcement learning, one of the main machine learning paradigms.

This course provides a comprehensive introduction to Reinforcement Learning (RL), covering fundamental concepts, key algorithms and practical applications.

Topics include:

  • Markov Decision Processes (MDPs),
  • multi armed bandit problems,
  • dynamic programming,
  • Monte Carlo methods,
  • temporal-difference learning,
  • policy gradient methods,
  • deep reinforcement learning,
  • The course is taught in English and consists of lectures covering theoretical foundations and key concepts in reinforcement learning.The course emphasizes both theoretical foundations and hands-on implementation, with programming exercises.
  • Exercise sessions focus on practical implementation, where students apply RL algorithms through coding exercises and problem-solving tasks.



Teaching Format

The course is taught in English and consists of lectures covering theoretical foundations and key concepts in reinforcement learning.

Exercise sessions focus on practical implementation, where students apply RL algorithms through coding exercises and problem-solving tasks.


Assessment

The course is examined through an on-campus written exam and assignments.

Examiner


The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.


Note that the course literature can be changed up to two months before the start of the course.


Course reports are displayed for the three most recent course instances.









Study counsellors

Margrét Håkansson and Mitra Wijkman

master@dsv.su.se
+468161686

Visiting hoursPlease contact us via email if you want to book a meeting. We are available on Campus in Kista and via Zoom.

Phone hoursThursday 12.30–2 pm