Cover Image

Sample-Efficient Reinforcement Learning of Koopman eNMPC

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mayfrank, Daniel, Velioglu, Mehmet, Mitsos, Alexander, Dahmen, Manuel
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit
by: Mayfrank, Daniel, et al.
Published: (2025)

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
by: Mayfrank, Daniel, et al.
Published: (2024)

End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
by: Mayfrank, Daniel, et al.
Published: (2023)

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
by: Schulze, Jan C., et al.
Published: (2024)

Graph neural networks for the prediction of molecular structure-property relationships
by: Rittig, Jan G., et al.
Published: (2022)

Robust Design of Multi-Energy Systems Accounting for Mixed-Integer Operational Problems
by: Wedemeyer, Moritz, et al.
Published: (2026)

Robust Energy System Design via Semi-infinite Programming
by: Wedemeyer, Moritz, et al.
Published: (2024)

Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
by: Alharbi, Meshal, et al.
Published: (2023)

Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning
by: Wan, Jia, et al.
Published: (2024)

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
by: Zhan, Donglin, et al.
Published: (2025)

TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimization
by: Sorokin, D., et al.
Published: (2023)

Koopman-Assisted Reinforcement Learning
by: Rozwood, Preston, et al.
Published: (2024)

Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?
by: Georgiou, Anastasia, et al.
Published: (2025)

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
by: Schulze, Jan C., et al.
Published: (2025)

Estimating Dense-Packed Zone Height in Liquid-Liquid Separation: A Physics-Informed Neural Network Approach
by: Velioglu, Mehmet, et al.
Published: (2026)

Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
by: Velioglu, Mehmet, et al.
Published: (2024)

Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning
by: Chen, Zijun, et al.
Published: (2025)

Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
by: Suttle, Wesley A., et al.
Published: (2024)

Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
by: Sherman, Uri, et al.
Published: (2025)

Time-certified Input-constrained NMPC via Koopman Operator
by: Wu, Liang, et al.
Published: (2024)

The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
by: Muehlebach, Michael, et al.
Published: (2025)

Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens
by: Moghaddam, Amirreza Neshaei, et al.
Published: (2024)

SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems
by: Strässer, Robin, et al.
Published: (2024)

Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
by: Cai, Qi, et al.
Published: (2022)

Sample-Efficient Agnostic Boosting
by: Ghai, Udaya, et al.
Published: (2024)

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response
by: Germscheid, Sonja H. M., et al.
Published: (2024)

Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
by: Qiu, Shuang, et al.
Published: (2024)

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
by: Xiong, Guojun, et al.
Published: (2024)

Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
by: de Jong, Thomas, et al.
Published: (2024)

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
by: Caldarelli, Edoardo, et al.
Published: (2024)

ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
by: Huang, Yilie, et al.
Published: (2026)

Resilient Constrained Reinforcement Learning
by: Ding, Dongsheng, et al.
Published: (2023)

Accuracy of Discretely Sampled Stochastic Policies in Continuous-time Reinforcement Learning
by: Jia, Yanwei, et al.
Published: (2025)

Residuals-based Offline Reinforcement Learning
by: Zhu, Qing, et al.
Published: (2026)

A Pontryagin Perspective on Reinforcement Learning
by: Eberhard, Onno, et al.
Published: (2024)

Foundations of Multivariate Distributional Reinforcement Learning
by: Wiltzer, Harley, et al.
Published: (2024)

Operator World Models for Reinforcement Learning
by: Novelli, Pietro, et al.
Published: (2024)

Progressively Sampled Equality-Constrained Optimization
by: Curtis, Frank E., et al.
Published: (2025)

Learning Spatio-Temporal Dynamics via Operator-Valued RKHS and Kernel Koopman Methods
by: Withanachchi, Mahishanka
Published: (2025)

On the Foundation of Distributionally Robust Reinforcement Learning
by: Wang, Shengbo, et al.
Published: (2023)