论文标题
DeepKoco：具有与任务相关的Koopman代表的有效潜在计划
DeepKoCo: Efficient latent planning with a task-relevant Koopman representation
论文作者
论文摘要
本文介绍了DeepKoco，这是一种基于模型的新型代理，从图像中学习了潜在的Koopman表示形式。这种表示允许DeepKoco使用线性控制方法（例如线性模型预测控制）有效地计划。与传统的代理相比，DeepKoco了解了与任务相关的动态，这要归功于使用量身定制的有损自动编码器网络，该网络允许DeepKoco学习重建和预测仅观察到的成本的潜在动态，而不是所有观察到的动态。正如我们的结果所示，DeepKoco的最终性能与复杂控制任务的传统无模型方法相似，同时对干扰器动态更加强大，这使得拟议的代理更适合现实生活中的应用。
This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images. This representation allows DeepKoCo to plan efficiently using linear control methods, such as linear model predictive control. Compared to traditional agents, DeepKoCo learns task-relevant dynamics, thanks to the use of a tailored lossy autoencoder network that allows DeepKoCo to learn latent dynamics that reconstruct and predict only observed costs, rather than all observed dynamics. As our results show, DeepKoCo achieves similar final performance as traditional model-free methods on complex control tasks while being considerably more robust to distractor dynamics, making the proposed agent more amenable for real-life applications.
