Seminar Reinforcement Learning


Reinforcement Learning is one of the three main learning principles in machine learning and one of the most active research areas in artificial intelligence. It is a computational approach to learning in which an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment.
This Bachelor seminar is based on the 2nd edition of the famous, seminal book on reinforcement learning written by Sutton and Barto ( The book introduces core topics of reinforcement learning from an artificial intelligence or engineering perspective, considering idealized learning situations, and evaluating the effectiveness of different learning methods. To effectively solve learning problems that are of scientific or economic interest, algorithms for machines are explored and evaluated through mathematical analysis and computational experiments. Compared to unsupervised or supervised learning approaches, reinforcement learning is more focused on goal-directed learning from interaction with the environment. The first part of the book addresses core concepts of reinforcement learning for problems with small state and action spaces, allowing for exact solutions using table-based methods. In the second part of the book these approaches are then extended using approximate methods for larger and more complex problems.

Learning Outcomes:

  • Knowledge on different reinforcement learning algorithms
  • Explain the underlying mathematical problem formulations and the implementation of the algorithms to solve them
  • Gain insight into how to frame learning problems in the reinforcement learning framework
  • Discuss practical applications of the theoretical frameworks
  • Present the algorithms and mathematical problem formulations to an audience

Teaching form:

In the seminar sessions students will present chapters of the book “Reinforcement Learning”, followed by discussions on the chapter topics.


Oral presentation and active participation


Places will be allocated by the Faculty:



Course type
Summer Term 2024
moodle course available


Takes place every week on Monday from 10:00 to 12:00 in room IA 02/111.
First appointment is on 08.04.2024
Last appointment is on 15.07.2024


Knowledge of calculus, linear algebra, and probability concepts. Background in artificial intelligence, e. g. via the course “Introduction to Artificial Intelligence”.

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