Presentation and written report (or exam, will be determined)

The lecture covers the whole range of reinforcement learning topics, from the basic formalism and theory to state-of-the-art algorithms.
The lecture is accompanied by exercises that help to deepen the mathematical understanding, as well as hands-on experience in implementing algorithms.

• Introduction to supervised learning and optimization
• Basics of Reinforcement Learning (RL) and Markov Decision Processes
• Dynamic programming, prediction and control
• Value function approximation
• Policy gradient
• Deep RL, control in continuous state-action domains
• Optimal control and model-based RL
• Advanced topics in RL

(1) Students can phrase a problem in the reinforcement learning framework and
can select an appropriate algorithm for solving it.
(2) Students are able to implement a set of deep reinforcement learning algorithms
and analyse their behavior.
(3) Students can explain the challenges in reinforcement learning and assess
and characterize new reinforcement learning methods.

Reinforcement learning by Sutton and Barto http://incompleteideas.net/
book/bookdraft2017nov5.pdf
Pattern Recognition and Machine Learning by C.M. Bishop, Chap. 3 and 5
Deep Learning by Goodfellow, Bengio and Courville https://www.
deeplearningbook.org / Recommended to attend basic Machine learning class before.