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This class is usually taught in the winter term. The class language is English.
Information for the current semester (if available):
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Class Objectives and Content
The lecture gives an introduction to the basics of reinforcement learning and covers the following topics:
- Introduction: Definition of the Reinforcement Learning Problem
- Introduction: Scientific Computing with Python
- Multi-armed Bandits
- Finite Markov Decision Processes
- Dynamic Programming
- Monte Carlo Methods
- Temporal-Difference Learning
- Policy Gradient Methods
- Deep Reinforcement Learning (A Selection)
- Selected Applications and Examples of Reinforcement Learning
The content of this lecture is mainly based on the book: "Reinforcement Learning: An Introduction." by Richard S. Sutton and Andrew G. Barto.
A draft of the book should be available here, opens an external URL in a new window.
There will also be complementary practical exercises to improve the understanding of the material taught in class, in the form of homework assignments. The assignments consist of analytical problems and programming exercises. Programming exercises will be solved using the Python programming language (no prior knowledge is required, there will be a short introduction in the first lecture).
