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.
@article{arxiv.2110.11265,
title = {Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations},
author = {Erfan Pirmorad and Faraz Khoshbakhtian and Farnam Mansouri and Amir-massoud Farahmand},
journal= {arXiv preprint arXiv:2110.11265},
year = {2021}
}