Methods of Modern Reinforcement Learning

IMPORTANT:
Seats are limited and the registration is managed via Moodle. The course "Master-Seminar: Methods of Modern Reinforcement Learning" will be available for application from 9.3.2020 to 30.3.2020. Seats will be allocated in first-come, first-serve manner, Master's students "Angewandte Informatik" will be treated with priority.

When registering, include your current program in the comment field (e.g., "Master Angewandte Informatik").

Reinforcement Learning (RL) describes the highly active and diverse field of learning optimal behavior from interactions with a changing environment, e.g. achieving expert level performance in computer games just by playing them. Conceptually, RL is considered to lie between supervised and unsupervised machine learning approaches and makes use of techniques from both fields.

In this course, you will get to know the tools of modern RL that led to the 2013 breakthrough result of learning to play computer games at super-human level from visual input only. Afterwards, recent and significant improvements to the method as well as current results will be discussed by directly working with the relevant research papers.

After the successful completion of this course, the students

  • understand the framework and basic methods for reinforcement learning,
  • understand core challenges of the field, and
  • will be able to contextualize current publication with respect to those challenges.

Lecturers

Details

Course type
Seminars
Credits
3
Term
Summer Term 2020

Requirements

We expect a solid level of mathematics as taught in the Applied Computer Science Bachelor‘s. Tools commonly used in machine learning are

  • basic probability theory/statistics (expectations, variance, foundational distributions and densities, markov chains)
  • linear algebra (matrices, vectors, eigenvalues/eigenvectors)
  • calculus (functions, derivatives/gradients, simple integrals)

The course material is in English, the course language will be English or German – depending on the students.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210