Koopman Kernel Regression

1 code implementation • NeurIPS 2023 • Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski, Eyke Hüllermeier, Sandra Hirche

Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of interest, e. g., the state of an agent or the reward of a policy.

Decision Making regression

Towards Data-driven LQR with Koopmanizing Flows

no code implementations • 27 Jan 2022 • Petar Bevanda, Max Beier, Shahab Heshmati-Alamdari, Stefan Sosnowski, Sandra Hirche

To utilize it for efficient LTI control design, we learn a finite representation of the Koopman operator that is linear in controls while concurrently learning meaningful lifting coordinates.

Diffeomorphically Learning Stable Koopman Operators

1 code implementation • 8 Dec 2021 • Petar Bevanda, Max Beier, Sebastian Kerz, Armin Lederer, Stefan Sosnowski, Sandra Hirche

System representations inspired by the infinite-dimensional Koopman operator (generator) are increasingly considered for predictive modeling.

Operator learning