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Title:

Koopman Kernel Regression

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
P. Bevanda, M. Beier, A. Lederer, S. Sosnowski, E. Hüllermeier, S. Hirche
Abstract:
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. Forecasts of such complex phenomena are commonly described by highly nonlinear dynamical systems, making their use in optimization-based decision-making challenging. Koopman operator theory offers a beneficial paradigm for addressing this problem by characteriz...     »
Keywords:
Data-Driven Models, relAI
Dewey Decimal Classification:
000 Informatik, Wissen, Systeme
Horizon 2020:
SeaClear
Book / Congress title:
Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
Year:
2023
Bookseries title:
NeurIPS Proceedings
Reviewed:
ja
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