Markov Decision Processes in Artificial Intelligence (English)

In the recent years, we have witnessed spectacular progress in applying techniques of reinforcement learning to problems that have for a long time considered to be out-of-reach -- be it the game of “Go” or autonomous driving. This course is about Markov decision processes, which is the mathematical foundation of reinforcement learning. The style of the course will be two-fold. On the one hand, the lecture will provide rigorous definitions and proves for the most central motives in Markov decision processes. On the other hand, this theory will be illustrated by hands-on implementations reflecting the most recent developments in this fast-moving field.


  • Deep Q-learning
  • Policy Gradient Theorem
  • Value and Policy Iteration
  • Linear-Quadratic-Gaussian Control
  • Kalman Filter
  • Multi-Armed Bandits
  • Stochastic Approximation Algorithms