In many areas, such as the physical sciences, life sciences, and finance, control approaches are used to achieve a desired goal in complex dynamical systems governed by differential equations. In this work we formulate the problem of controlling stochastic partial differential equations (SPDE) as a reinforcement learning problem. We present a learning-based, distributed control approach for online control of a system of SPDEs with high dimensional state-action space using deep deterministic policy gradient method. We tested the performance of our method on the problem of controlling the stochastic Burgers' equation, describing a turbulent fluid flow in an infinitely large domain.

翻译：在许多领域,如物理科学、生命科学和金融领域,控制方法被用于在受差异方程式制约的复杂动态系统中实现预期目标。在这项工作中,我们把控制随机偏差部分方程式(SPDE)作为一个强化学习问题提出问题。我们提出了一种基于学习的分散控制方法,用于利用深层确定性政策梯度方法,对具有高维度状态-行动空间的SPDE系统进行在线控制。我们测试了我们方法在控制随机布尔格斯方程式问题上的性能,描述了无限大范围内的动荡流流。