Model Predictive Control and Reinforcement Learning

Block Course, 06.10.2025 - 10.10.2025, 9:00-18:00

Lecturers: Prof. Dr. Joschka Boedecker (Uni Freiburg), Prof. Dr. Moritz Diehl (Uni Freiburg)

Exercises: Leonard Fichtner, Andrea Ghezzi and Jasper Hoffmann

Contacts: for any questions feel free to contact us mpcrl@cs.uni-freiburg.de.

Locations: Kollegiengebäude I, HS 1199, Platz der Universität 3, 79098 Freiburg, Google Maps

After a one-year break, we are excited to announce the fourth edition of this block course, building on the success of the previous editions (MPCRL23, MPCRL22, MPCRL21)!

This comprehensive course spans 5 days, in the first two days we will cover the foundation of both MPC and RL, and the in the remainder of the week we want to focus on the combination of MPC and RL.

The program will follow (approximately) the topics covered in our recent survey paper.

Lectures will be supported with intensive exercise/programming sessions. 

Ultimately, the 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.

Many projects developed in the past has led to peer-reviewed publications!

 

Course topics (may be subject to updates)

  • 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)
  • Reinforcement Learning (RL) formulations and approaches  
  • Nonlinear Model Predictive Control
  • When to use RL in MPC?
  • Differentiable MPC within Actor-Critic methods
  • Closed-loop tuning of MPC with RL
  • Overview of possible synergies between MPC and RL

 

A more detailed program will be shared soon!

Registration will open soon!

Registration: within August 31, 2025, 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: 350 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.

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 students from the University of Freiburg
  • We strongly recommend the students to have taken at least two of the following courses: Numerical Optimization (Diehl, 6 ECTS), Numerical Optimal Control (Diehl, 6 ECTS), and Reinforcement Learning (Boedecker, 6 ECTS)
  • The evaluation of the course will be based on the exercise sessions and the project works. Further details on evaluation will be published soon.

Formal requirements

Relevant only for students of the university of Freiburg.

In order to receive 3 ECTS for this course, students need to pass all of the following:

  • Studienleistung (SL, ungraded)
    • Participation in the exercise session
  • Prüfungsleistung (PL, graded)
    • Project report

Every student from the University of Freiburg needs to fill out the registration form.

Please also read the project instructions from above.

On the first day (October 4th), students further need to decide whether they want to commit themselves to do the PL. The registration will take via the PL registration form below.

 

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.