Jasper Hoffmann

PhD Candidate

University of Freiburg

Jasper Hoffmann obtained his bachelor’s degree in mathematics in 2016 and his master’s degree in computer science in 2020 at the University of Freiburg. His master thesis was about the interference of function approximation in temporal differencing methods (reinforcement learning). From there he worked as a research assistant at the Computer Vision group Freiburg on neural network compression and out-of-distribution robustness. From there he started his PhD in June 2021 at the Neurorobotics Lab in Freiburg. He is especially interested in combining Model Predictive Control and Reinforcement Learning.

Project description

Classical planning methods can effectively control a single car by leveraging models of the car physics but have difficulties with the uncertainty and interaction complexity of multi-agent human driving required in fully autonomous driving. Thus, this PhD project is about extending optimal control methods with deep reinforcement learning to tackle these challenges. Important steps during the project are finding the right algorithmic framework and training method as well as successfully doing the transfer from simulation to the real system. Possible research directions could include offline reinforcement learning, planning on learned latent space models, or using differentiable optimization layers.


Ghezzi, Andrea; Hoffman, Jasper; Frey, Jonathan; Boedecker, Joschka; Diehl, Moritz

Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation Proceedings Article Forthcoming

In: 2023 Conference on Decision and Control (CDC), Forthcoming.

Abstract | Links | BibTeX


Reiter, Rudolf; Hoffman, Jasper; Boedecker, Joschka; Diehl, Moritz

A Hierarchical Approach for Strategic Motion Planning in Autonomous Racing Proceedings Article

In: 2023 European Control Conference (ECC), pp. 1-8, IEEE, Bucharest, Romania, 2023, ISBN: 978-3-907144-08-4.

Abstract | Links | BibTeX