We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator and associated linear controller within an iterative loop. By prioritizing the task cost as main objective for controller learning, we reduce the reliance of controller design on a well-identified model, which extends Koopman control beyond low-dimensional systems to high-dimensional, complex nonlinear systems, including pixel-based scenarios.

翻译：我们提出了一种面向任务的Koopman控制方法，该方法利用端到端强化学习与对比编码器，在迭代优化框架中同时学习Koopman潜空间嵌入、算子及其相关的线性控制器。通过将任务代价作为控制器学习的主要目标，我们降低了控制器设计对精确辨识模型的依赖性，从而将Koopman控制从低维系统拓展至包括像素级场景在内的高维复杂非线性系统。