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Titel:

Koopman Kernel Regression

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
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...     »
Stichworte:
Data-Driven Models, relAI
Dewey-Dezimalklassifikation:
000 Informatik, Wissen, Systeme
Horizon 2020:
SeaClear
Kongress- / Buchtitel:
Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
Jahr:
2023
Serientitel:
NeurIPS Proceedings
Reviewed:
ja
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