Deep Reinforcement Learning for Online Control of Stochastic Partial Differential EquationsDownload PDF

27 Sept 2021, 22:32 (modified: 08 Dec 2021, 18:01)DLDE Workshop -- NeurIPS 2021 SpotlightReaders: Everyone
Keywords: Deep Reinforcement Learning, Control, Stochastic Partial Differential Equations (SPDE)
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Publication Status: This work is unpublished.
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