We present a method for end-to-end learning of Koopman surrogate models for optimal performance in control. In contrast to previous contributions that employ standard reinforcement learning (RL) algorithms, we use a training algorithm that exploits the potential differentiability of environments based on mechanistic simulation models. We evaluate the performance of our method by comparing it to that of other controller type and training algorithm combinations on a literature known eNMPC case study. Our method exhibits superior performance on this problem, thereby constituting a promising avenue towards more capable controllers that employ dynamic surrogate models.

我们提出了一种用于控制中最优性能的Koopman代理模型端到端学习的方法。与以往使用标准强化学习算法的方法不同，我们使用一种训练算法，该算法利用了基于机械模拟模型的环境的潜在可区分性。通过将我们的方法与其他控制器类型和训练算法组合在文献已知的eNMPC案例研究中进行比较，我们评估了我们方法的性能。我们的方法在这个问题上表现出卓越的性能，从而为采用动态代理模型的更可靠的控制器开辟了一个有希望的途径。

基于可微分仿真与优化的任务最优数据驱动代替模型