6 ECTS credits
150 h study time

Offer 1 with catalog number 4013067FNR for all students in the 1st and 2nd semester at a (F) Master - specialised level.

Semester
1st and 2nd semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Enrollment Requirements
Registration for 'Multi-Agent Learning Seminar' is allowed if one is registered for or has successfully accomplished 'Multi-Agent Learning' or 'Computational Game Theory'.
Taught in
English
Partnership Agreement
Under agreement for exchange of courses
Faculty
Faculty of Sciences and Bioengineering Sciences
Department
Computer Science
Educational team
Ann Nowe (course titular)
Activities and contact hours

26 contact hours Lecture
26 contact hours Seminar, Exercises or Practicals
Course Content

The course will serve as an introduction to the basic concepts of single agent RL and slowly build up to advanced concepts leading to current state-of-the-art methods and Deep RL. More detailed information about the content can be found in canvas.

Course material
Digital course material (Required) : Papers will be made available, http://ai.vub.ac.be/courses/2017-2018/multi-agent-learning-seminar/
Additional info

http://ai.vub.ac.be/courses/2020-2021/reinforcement_learning
 

Learning Outcomes

Algemene competenties

knowledge and insight
The student has knowledge and insight in the domain of learning systems which allows him to possibly provide an original contribution to the domain.

The use of knowledge and insight
The student can combine the ideas covered in the course to obtain a suitable approach for a new problem.

Judgment ability
The student can judge autonomously the scientific papers in this domain. 

Communication
The student can present the content of their final project to the other students and communicate his ideas on the solutions.

 
Skills
The student can autonomously search, read and implement papers in this area of research. 

Grading

The final grade is composed based on the following categories:
LEC Teamwork determines 50% of the final mark.
PRAC Practical Assignment determines 50% of the final mark.

Within the LEC Teamwork category, the following assignments need to be completed:

  • Participation in class with a relative weight of 1 which comprises 50% of the final mark.

    Note: The student is evaluated based on his participation to the discussions during the contact hours

Within the PRAC Practical Assignment category, the following assignments need to be completed:

  • Presentations + assignments with a relative weight of 1 which comprises 50% of the final mark.

    Note: The student is evaluated based on presentations + assignment.

Additional info regarding evaluation

Students will be evaluated based on following criteria:

• Course participation (each student will have to hand in solutions to at least 2 course exercises + participation in discussions). 10% (mandatory submission of exercises, at least 10/20 grade)
• Learning agent project (2nd semester)
• Project defense & report (2nd semester exam period) 90%

Academic context

This offer is part of the following study plans:
Master in Applied Sciences and Engineering: Computer Science: Artificial Intelligence (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Multimedia (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering (only offered in Dutch)
Master in Applied Sciences and Engineering: Computer Science: Data Management and Analytics (only offered in Dutch)
Master of Applied Sciences and Engineering: Computer Science: Artificial Intelligence
Master of Applied Sciences and Engineering: Computer Science: Multimedia
Master of Applied Sciences and Engineering: Computer Science: Software Languages and Software Engineering
Master of Applied Sciences and Engineering: Computer Science: Data Management and Analytics