Despite the existence of formal guarantees for learning-based control approaches, the relationship between data and control performance is still poorly understood. In this paper, we present a measure to quantify the value of data within the context of a predefined control task. Our approach is applicable to a wide variety of unknown nonlinear systems that are to be controlled by a generic learning-based control law. We model the unknown component of the system using Gaussian processes, which in turn allows us to directly assess the impact of model uncertainty on control. Results obtained in numerical simulations indicate the efficacy of the proposed measure.

中文翻译：

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数据在基于学习的控制中训练子集选择的价值

尽管存在基于学习的控制方法的正式保证，但对数据与控制性能之间的关系仍然知之甚少。在本文中，我们提出了一种在预定义的控制任务范围内量化数据值的措施。我们的方法适用于各种未知的非线性系统，这些系统将通过基于学习的通用控制律进行控制。我们使用高斯过程对系统的未知组件进行建模，这又使我们能够直接评估模型不确定性对控制的影响。在数值模拟中获得的结果表明了该措施的有效性。