论文标题
学习稳定的深度动态模型
Learning Stable Deep Dynamics Models
论文作者
论文摘要
深网通常用于建模动态系统，预测系统的状态将如何随着时间的流逝而发展（无论是自主还是响应控制输入）。尽管这些系统具有预测能力，但很难就学习系统的基本特性做出正式主张。在本文中，我们提出了一种学习动态系统的方法，该方法可以保证在整个状态空间中保持稳定。该方法通过共同学习动力学模型和Lyapunov函数来确保在学识渊博的Lyapunov函数下的非表达性。我们表明，此类学习系统能够建模简单的动力系统，并可以与其他深层生成模型结合使用，以完全端到端的方式学习复杂的动态，例如视频纹理。
Deep networks are commonly used to model dynamical systems, predicting how the state of a system will evolve over time (either autonomously or in response to control inputs). Despite the predictive power of these systems, it has been difficult to make formal claims about the basic properties of the learned systems. In this paper, we propose an approach for learning dynamical systems that are guaranteed to be stable over the entire state space. The approach works by jointly learning a dynamics model and Lyapunov function that guarantees non-expansiveness of the dynamics under the learned Lyapunov function. We show that such learning systems are able to model simple dynamical systems and can be combined with additional deep generative models to learn complex dynamics, such as video textures, in a fully end-to-end fashion.
