Reinforcement Learning
Syllabus, Master's level, 1RT747
- Code
- 1RT747
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Computer Science A1N, Data Science A1N, Embedded Systems A1N, Image Analysis and Machine Learning A1N
- Grading system
- Pass with distinction (5), Pass with credit (4), Pass (3), Fail (U)
- Finalised by
- The Faculty Board of Science and Technology, 27 February 2020
- Responsible department
- Department of Information Technology
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.
Learning outcomes
On completion of the course, the student should be able to:
- discuss possibilities and limitations of reinforcement learning.
- discuss 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 case define it formally.
- 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, learning with function approximations and 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 (2.5 credits), project presentation (2 credits), and an oral 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.