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FDD3359 Reinforcement Learning 6.0 credits

About course offering

For course offering

Spring 2024 Start 16 Jan 2024 programme students

Target group

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Part of programme

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P3 (6.0 hp)


16 Jan 2024
15 Mar 2024

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Campus

Number of places

Places are not limited

Planned modular schedule


For course offering

Spring 2024 Start 16 Jan 2024 programme students

Application code



For course offering

Spring 2024 Start 16 Jan 2024 programme students


Johannes Stork


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Course coordinator

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Headings with content from the Course syllabus FDD3359 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The following fields, among others, will be treated:

Reinforcement learning with known and unknown models, discrete and continuous dynamic systems, Markov process formalism, Bellman optimality principle, exact and approximate algorithms, proofs of convergence, action policies, MDPs, discounted MDPs, POMDPs, reinforcement learning with temporal difference, Monte Carlo, and Q-learning.

The course also includes components, where students should prepare a lecture as well as develop a laboratory session where other students participate.

Intended learning outcomes

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 a number of basic methods in reinforcement learning

* evaluate different learning algorithms experimentally and interpret and document results of experimental studies

* choose appropriate method to process automatically various types of data as e g sensor data that are used in controlling algorithms

* account for basic methods and limitations in reinforcement learning

* build a toolbox of different algorithms and be able to apply these on real problems

in order to

* be familiar with basic possibilities and limitations for reinforcement learning and thereby be able to assess which problems in e g robot movement regulation and automatic decision-making that can be solved with these technologies

* be able to implement, analyse and evaluate simple systems based on reinforcement learning

* have a broad knowledge to be able to read and profit by literature in the area.

Literature and preparations

Specific prerequisites

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Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • EXA1 - Examination, 6.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

SEM1 (4 credits, P/F), PRO1 (2 credits, P/F)

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted


Johannes Stork

Postgraduate course

Postgraduate courses at EECS/Robotics, Perception and Learning