The content discusses the application of end-to-end reinforcement learning of Koopman models for optimal performance in economic nonlinear model predictive control (NMPC). It introduces a method to train dynamic surrogate models using reinforcement learning algorithms, focusing on task-optimal performance. The study compares the effectiveness of models trained through system identification and reinforcement learning techniques. Results demonstrate that end-to-end trained models outperform those trained using traditional methods, showcasing adaptability to changes in control settings without retraining.
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by Daniel Mayfr... alle arxiv.org 03-25-2024
https://arxiv.org/pdf/2308.01674.pdfDomande più approfondite