Task-optimal data-driven surrogate models for eNMPC via differentiable
  simulation and optimization
Daniel Mayfrank Na Young Ahn Alexander Mitsos Manuel Dahmen
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.