Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning
<|reference_start|>Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning: The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system's governing equations, especially in complex environments that combine physical laws of different disciplines. In this paper, we present a new approach that allows identifying an ordinary differential equation by means of a physics-informed machine learning algorithm. Our method introduces a special neural network that allows exploiting prior human knowledge to a certain degree and extends it autonomously, so that the resulting differential equations describe the system as accurately as possible. We validate the method on a Duffing oscillator with simulation data and, additionally, on a cascaded tank example with real-world data. Subsequently, we use the developed algorithm in a model-based reinforcement learning framework by alternately identifying and controlling a system to a target state. We test the performance by swinging-up an inverted pendulum on a cart.<|reference_end|>
@article{nagel2024identifying,
    title={Identifying Ordinary Differential Equations for Data-efficient
  Model-based Reinforcement Learning},
    author={Tobias Nagel and Marco F. Huber},
    journal={arXiv preprint arXiv:2406.19817},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19817},
    primaryClass={eess.SY cs.SY}
}