Course Syllabus for

Study Circle in Deep Reinforcement Learning
Studiecirkel om Deep Reinforcement Learning

FRT240F, 5 credits

Valid from: Spring 2021
Decided by: Professor Thomas Johansson
Date of establishment: 2021-02-18

General Information

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


To teach the participants basic topics in deep reinforcement learning


Knowledge and Understanding

For a passing grade the doctoral student must understand advanced topics in reinforcement learning and implementation aspects of deep reinforcement learning using neural networks.

Competences and Skills

For a passing grade the doctoral student must be able to implement different deep reinforcement learning techniques based on Neural Networks.

Judgement and Approach

For a passing grade the doctoral student must

Course Contents

Q functions, Advanced Policy Gradients, Model Based planning, Model Based RL, Model Based Policy Learning, Control as Inference, Inverse Reinforcement Learning, Distributed RL, Challenges and Open problems

Course Literature

Sutton, Richard S. & Barto, Andrew G.: Reinforcement Learning, Second Edition. ISBN 9780262039246.

Instruction Details

Types of instruction: Seminars, exercises, self-study literature review, miscellaneous. based on Berkely deep RL course "CS285" and a few assignments available on Github

Examination Details

Examination format: Miscellaneous. Active participation in the course
Grading scale: Failed, pass

Admission Details

Admission requirements: Admitted to PhD program at an engineering faculty
Assumed prior knowledge: Basic reinforcement learning knowledge, basic neural network construction and training using tensorflow or pytorch
Selection criteria: None

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page:

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