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.

翻译：我们提出了一种用于控制中优化性能的Koopman代理模型端到端学习方法。与以往采用标准强化学习算法的研究不同，我们使用的训练算法充分利用了基于机理仿真模型环境的潜在可微性。通过将我们的方法与文献中已知的eNMPC案例研究中其他控制器类型及训练算法组合的性能进行对比，我们评估了所提方法的有效性。实验表明，该方法在该问题上展现出更优越的性能，因此为开发采用动态代理模型的更强大控制器开辟了一条有前景的途径。