Reinforcement Learning
This course introduces basic as well as modern concepts of reinforcement learning.
Please note that the english syllabus is incorrect - instead of being a translation of the Swedish syllabus, most of the information is a copy from another course. See instead the information below. For technical reasons (a change in the system where the syllabus is generated) we are currently unable to correct the syllabus.
Contents
The aim of the course is to introduce basic as well as modern concepts of reinforcement learning. This includes Markov decision processes, dynamic programming, model-free prediction and control, temporal difference learning, function approximation methods, policy gradient and actor-critic methods, and modern applications of reinforcement learning.
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Course structure
The course consists of two modules: theory (3.5 credits) and project (4 credits).
Teaching format
Instruction is given in the form of lectures, exercise sessions and supervision.
Assessment
The course is assessed through a written exam, and project assignment.
Both parts of the course are graded on a scale from A to F, where A to E are passing grades. To complete the course, a passing grade is required on both parts, and the final grade of the course is determined by weighing the grades from the course modules, where each grade is weighed in relation to the scope of the course module.
Examiner
A list of examiners can be found on
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Schedule
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. -
Course literature
Note that the course literature can be changed up to two months before the start of the course.
The course literature is not yet formally decided, but here is the suggested literature:
"Reinforcement Learning: An introduction", R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press, Cambridge, MA (2018) (e-book available via the university library)
"An Introduction to Deep Reinforcement Learning", V. François-Lavet, P. Henderson, R. Islam, M.G. Bellemare, J. Pineau, Foundations and Trends in Machine Learning: Vol. 11: No. 3-4, pp 219-354 (2018) (available for download at arxiv.org)
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Course reports
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More information
New student
During your studiesCourse web
We do not use Athena, you can find our course webpages on kurser.math.su.se.
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Contact
