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.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2021
- DOI:
- 10.48550/arXiv.2110.11265
- arXiv:
- arXiv:2110.11265
- Bibcode:
- 2021arXiv211011265P
- Keywords:
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- Computer Science - Machine Learning;
- Mathematics - Dynamical Systems