Reinforcement Learning
Team
Prof. Dr. Verena Wolf (lecturer)
Felix Jahn, Joschka Groß (assistants)
Reinforcement Learning
Reinforcement learning is an area of machine learning where the goal is to develop (near-)optimal policies for solving sequential decision-making problems. The policy is typically represented by an agent who learns to achieve a goal by interacting with the environment. RL is often seen as the third area of machine learning (in addition to supervised and unsupervised learning), in which training samples are generated as a result of the agent’s actions and interaction with the environment. In recent years, there have been remarkable successes in reinforcement learning research in both theoretical and applied fields. These successes are mostly the result of a new development in the field: representing policies by artificial neural networks allows us to solve much more complex decision problems.
Course Content
This course provides a broad introduction to reinforcement learning and its applications. You learn about Markov Decision Processes as the underlying formal framework for decision-making problems, as well as popular reinforcement learning algorithms such as Monte-Carlo methods, temporal difference methods, and different “deep” reinforcement learning approaches. We use the open-source Python library Gymnasium to train RL agents in different pre-built environments.
Recommended prerequisites are Programming 1, Programming 2 and basic knowledge of probability theory, as taught for example in Mathematics for Computer Scientists 3.
Format
The course is a 6 ECTS advanced lecture with weekly lectures, bi-weekly assignment sheets with theoretical and programming exercises, bi-weekly tutorials and office hours, and a 90-minute final exam. Admission to the exam requires 50% of the points of the assignment sheets.
Literature
- Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction
- Csaba Szepesvári: Algorithms for Reinforcement Learning
The lecture ran again in Winter 2025/26.