Computational Game Theory (Learning Dynamics)

Time: 

The lecture will take place every thursday starting at 13:00 in room 10F720 (see schedule below)

Objectives: 

The aim of the course is to introduce the students to the field of learning in individual agents and learning in populations of agents and to prepare them for a Master thesis in these research areas.

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.

Prerequisites: 

none

Description: 

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 a single agent setting and introduce reinforcement learning as a model free approach to dynamic programming. Then we have a look at 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 which can include, for those who are interested,  experiments using the Khepera robots.

Examination: 
  • During the course sessions, students will receive assignments, of which the results are returned as a report during one of the following sessions.These assignments will be graded and the points will be used in the calculation of the final course grade.
  • Students will have to select a project that needs to be finished before the exam period in january. This year's projects can be found here!
  • The quality of the project and the quality of the defense (together with the points for the assignments) will determine the final points for this course.
 
The project proposals and the explanation can be found through this link
Schedule: 

(preliminary)