Infinite Dimensional系统随机控制的变分推断
Variational Inference for Stochastic Control of Infinite Dimensional Systems
摘要：
本文发展了一个用于无穷维随机系统控制的变分推理框架。我们采用的措施理论的方法，它依赖于Girsanov定理的推广，以及相对熵和自由能之间的关系。所导出的控制方案适用于一大类随机无穷维系统，并可用于轨迹优化和模型预测控制。我们的工作开辟了新的研究途径，在随机控制，推理和信息论的随机偏微分方程描述的动力系统的交叉点。
英文摘要：
This paper develops a variational inference framework for control of infinite dimensional stochastic systems. We employ a measure theoretic approach which relies on the generalization of Girsanov's theorem, as well as the relation between relative entropy and free energy. The derived control scheme is applicable to a large class of stochastic, infinite dimensional systems, and can be used for trajectory optimization and model predictive control. Our work opens up new research avenues at the intersection of stochastic control, inference and information theory for dynamical systems described by stochastic partial differential equations.
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