6 ECTS credits
150 h study time
Offer 2 with catalog number 4013088ENR for all students in the 1st semester at a (E) Master - advanced level.
The aim of the course is to introduce the students to the field of Multi-agent learning and learning in populations of agents.
He or she will learn the basic principles of both domains, the mathematical and computational methods and the typical problems they are trying to solve.
The students will also obtain a basic understanding of (evolutionary) game theory which will allow them to understand the standard literature in that field and the relevance of this domain to learning in general.
The students will obtain the skills to address independently problems within these fields.
In addition, they will be capable of presenting their work to an audience of specialists and non-specialists.
The course addresses two general areas of research : individual-based learning and social learning in populations.
The first part focusses on learning through experience, of which reinforcement learning is the standard example. We start from simple single agent reinforcement learning (stateless RL) which we extent to deal with the interplay of multiple learning agents in the same environment. For this purpose and for the following part on evolutionary dynamics, basic concepts of Game Theory are introduced.
The second part provides an introduction to the principles of learning by imitation, modeled through evolutionary dynamics. It will explain what evolution is and how games can be used to model interactions between individuals in a population. It will show how these models can be used to study the evolution of cooperation in social dilemmas, the evolution of conventions like language or even the dynamics of cancer.
The course concludes with a project where students in small teams have to reproduce the reults of a scientific paper, discus and extend the results.
Course schedule, course material and information on assignments and project: http://www.ulb.ac.be/di/map/tlenaert/Home_Tom_Lenaerts/INFO-F-409.html
Knowledge and insight
The student knows different Reinforcement Learning (RL) techniques. The student can judge different exploration strategies, is knowledgeable about the exploration/exploration trade off. The student has insight in the dynamics of multiagent reinforcement learning and the relevancy of the basic concepts of Game Theory in this context.
Application of knowledge and insight
The student can solve a concrete problem using multiagent reinforcement learning technique.
Judgment ability
The student can give arguments why a given problem is suited or not for RL.
Communication
The students must complete 3 written assignments and 1 project in a team of 3 ot 4 students that needs to be presented.
Skills
The student will be prepared to read autonomously the literature In this research domain.
The final grade is composed based on the following categories:
LEC Teamwork determines 34% of the final mark.
PRAC Practical Assignment determines 33% of the final mark.
SELF Paper determines 33% of the final mark.
Within the LEC Teamwork category, the following assignments need to be completed:
Within the PRAC Practical Assignment category, the following assignments need to be completed:
Within the SELF Paper category, the following assignments need to be completed:
The final score will be based on the active participation of the student during the lectures and in particular the efforts and results delivered for the assignments (including assignments on game theory and the discussion of a paper)
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
Master of Teaching in Science and Technology: computerwetenschappen (120 ECTS, Etterbeek) (only offered in Dutch)