Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations
Abstract
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.
Cite
Text
Pirmorad et al. "Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations." NeurIPS 2021 Workshops: DLDE, 2021.Markdown
[Pirmorad et al. "Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations." NeurIPS 2021 Workshops: DLDE, 2021.](https://mlanthology.org/neuripsw/2021/pirmorad2021neuripsw-deep/)BibTeX
@inproceedings{pirmorad2021neuripsw-deep,
title = {{Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations}},
author = {Pirmorad, Erfan and Khoshbakhtian, Faraz and Mansouri, Farnam and Farahmand, Amir-massoud},
booktitle = {NeurIPS 2021 Workshops: DLDE},
year = {2021},
url = {https://mlanthology.org/neuripsw/2021/pirmorad2021neuripsw-deep/}
}