Wednesday, October 04, 2023, 9:00 - Friday, October 13, 2023, 18:00

HS 0-26, Georges-Köhler-Allee 101 D-79110 Freiburg, and HS 1015, Kollegiengebäude I, Platz der Universität 3, D-79098 Freiburg

​Lecturers: Prof. Dr. Joschka Boedecker (Uni Freiburg), Prof. Dr. Moritz Diehl (Uni Freiburg) and Prof. Dr. Sebastien Gros (NTNU Trondheim)

Exercises: Andrea Ghezzi and Jasper Hoffmann

Contacts: for any questions feel free to contact  Andrea or Jasper

Locations:

• Faculty of Engineering from Oct 4 to 6: HS 0-26, Georges-Köhler-Allee 101, D-79110 Freiburg, Google Maps
• Historical University from Oct 9 to 13: Kollegiengebäude I, HS 1015, Platz der Universität 3, D-79098 Freiburg, Google Maps

We are pleased to announce the third edition of this block course, building on the success of the previous editions (MPCRL22, MPCRL21)!

This comprehensive course spans 8 days, divided into two weeks. During the first week (3 days), we will provide a solid foundation in MPC (Model Predictive Control) and RL (Reinforcement Learning). In the second week (5 days), we will delve into advanced methods. We will explore nonlinear MPC (NMPC), transformers, policy gradient methods, and techniques for combining MPC and RL.

Lectures will be supported with intensive exercise/programming sessions. 

In addition, in the last three days participants will work on their own project in the domain of MPC / RL helped by the professors and the tutors. The project work can be an exciting opportunity to share ideas and collaborate with other participants.

Checkout our flyer!

Registration is closed!

The registration is now closed but if you want to join there might be still few places available, for information reach out Andrea

Registration: within September 20, 2023, until the limit of 60 participants is reached (first come first served). The registration is recorded after the fee has been transferred and received.

Participation fee: 400 EUR (free of charge for master’s students from the University of Freiburg). The fee includes a welcome reception and a dinner with the participants. 

Cancellation policy: no refund possible.

Course topics

• Dynamic Programming (DP) concepts and algorithms - value iteration and policy iteration
• Linear Quadratic Regulator (LQR) and Riccati equations
• Dynamic Systems: Simulation and Optimal Control 
• Markov Decision Processes (MDP)
• Function approximation with Deep Learning
• Reinforcement Learning (RL) formulations and approaches  
• Value-based RL: Monte Carlo, temporal differences, Q-learning
• RL with function approximation - DQN
• Nonlinear Model Predictive Control
• Transformers
• Policy gradient and Actor-Critic methods
• Safety and Stability in MPCRL
• When to use RL in MPC?

Targeted audience

This block course is intended for master students and PhD students from engineering, computer science, mathematics, physics, and other mathematical sciences. 

For interested Master students:

• We accept registration only from master student from the University of Freiburg
• We strongly recommend the students to have taken: (Numerical Optimal or Numerical Optimal Control) or (Reinforcement Learning) courses
• The evaluation of the course will be based on the exercise sessions and the project works. Further details on evaluation will be published soon!

Further details available on the teaching page!

This course has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 953348.

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