论文标题
袜子：使用内核方法的随机最佳控制和可及性工具箱
SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods
论文作者
论文摘要
我们提出袜子，这是基于内核方法的数据驱动的随机最佳控制工具箱。袜子是数据驱动算法的集合，该算法将近似解决方案计算为具有任意成本和约束功能的随机最佳控制问题，包括随机可及性，该问题旨在确定系统将达到所需目标集的可能性，同时尊重预定义的安全约束。我们的方法依赖于基于内核方法的一类机器学习算法，这是一种非参数技术，可用于在高度函数的高度函数空间中表示概率分布，称为繁殖核Hilbert Space。作为一种非参数技术，内核方法本质上是数据驱动的，这意味着它们没有对系统动力学或不确定性结构进行先前的假设。这使该工具箱适合各种系统，包括具有非线性动力学的系统，黑框元素和不良特征的随机干扰。我们介绍了袜子的主要特征，并在几个基准测试中展示了其功能。
We present SOCKS, a data-driven stochastic optimal control toolbox based in kernel methods. SOCKS is a collection of data-driven algorithms that compute approximate solutions to stochastic optimal control problems with arbitrary cost and constraint functions, including stochastic reachability, which seeks to determine the likelihood that a system will reach a desired target set while respecting a set of pre-defined safety constraints. Our approach relies upon a class of machine learning algorithms based in kernel methods, a nonparametric technique which can be used to represent probability distributions in a high-dimensional space of functions known as a reproducing kernel Hilbert space. As a nonparametric technique, kernel methods are inherently data-driven, meaning that they do not place prior assumptions on the system dynamics or the structure of the uncertainty. This makes the toolbox amenable to a wide variety of systems, including those with nonlinear dynamics, black-box elements, and poorly characterized stochastic disturbances. We present the main features of SOCKS and demonstrate its capabilities on several benchmarks.
