Syllabus for Reinforcement Learning
Förstärkningsinlärning
Syllabus
- 7.5 credits
- Course code: 1RT747
- Education cycle: Second cycle
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Main field(s) of study and in-depth level:
Data Science A1N,
Image Analysis and Machine Learning A1N,
Embedded Systems A1N,
Computer Science A1N
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
First cycle
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
Second cycle
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2020-02-27
- Established by:
- Revised: 2022-02-04
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2022
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Entry requirements:
120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus and 10 credits of programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Responsible department: Department of Information Technology
Learning outcomes
On completion of the course, the student should be able to:
- explain possibilities and limitations of reinforcement learning,
- explain the connection between reinforcement learning and feedback control,
- analyse relevant applications, decide if they can be formulated as a reinforcement learning problem, and in such a case formulate it,
- implement and use central algorithms for reinforcement learning,
- analyse and evaluate methods through different performance criteria,
- implement, evaluate and present for the course relevant methods from the research literature.
Content
This course gives a solid introduction to the modern tools used to devise, implement and analyse reinforcement learning algorithms. The course covers Markov decision processes, feedback control systems, planning by dynamic programming, model-free prediction and control, the trade-off between exploration and exploitation, function approximations and policy-gradient methods. It also introduces deep reinforcement learning. Applications discussed in the course include classical control problems such as the inverted pendulum, as well as robotics and computer games.
Instruction
Lectures, seminars, computer labs, project.
Assessment
Oral and written examination of assignments (2 credits), project presentation (2.5 credits), and a written examination (3 credits).
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
Other directives
The course cannot be included in the same degree as 1RT745 Reinforcement Learning.
Syllabus Revisions
- Latest syllabus (applies from Autumn 2022)
- Previous syllabus (applies from Autumn 2020)
Reading list
Reading list
Applies from: Autumn 2022
Some titles may be available electronically through the University library.
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Sutton, Richard S.;
Barto, Andrew G.
Reinforcement learning : an introduction
Second edition: Cambridge, MA: The MIT Press, [2018]