Course Syllabus for

Study Circle in Reinforcement Learning
Studiecirkel om Reinforcement Learning

FRT205F, 5 credits

Valid from: Spring 2019
Decided by: Professor Thomas Johansson
Date of establishment: 2019-09-12

General Information

Division: Automatic Control
Course type: Third-cycle course
Teaching language: English


To become familiar with reinforcement learning.


Knowledge and Understanding

For a passing grade the doctoral student must - understand the basic concepts and methods of reinforcement learning

Competences and Skills

For a passing grade the doctoral student must - be able to understand the implementation of reinforcement learning using Python and Tensorflow

Judgement and Approach

For a passing grade the doctoral student must - understand for which problems reinforcement learning is suitable

Course Contents

- Markov Decision Processes - Dynamic programming - Iterative policy evaluation - Iterative policy iteration - Value iteration - Model-free planning - Monte-Carlo methods - Model-free control - Value Function Approximation - Policy Gradient Methods - Exploration and Exploitation

Course Literature

The course is based on video lectures by David Silver.

Instruction Details

Types of instruction: Seminars, exercises, self-study literature review, miscellaneous. Based on the Reinforcement Learning Course given by David Silver at UCL and the associated video presentations.

Examination Details

Examination format: Miscellaneous. Sufficient active participation in the course meetings
Grading scale: Failed, pass

Admission Details

Admission requirements: Admitted to PhD program at an engineering faculty
Assumed prior knowledge: None
Selection criteria: None

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page:

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