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Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations
Erfan Pirmorad · Farnam Mansouri · Amir-massoud Farahmand

Tue Dec 14 11:30 AM -- 12:15 PM (PST) @
Event URL: https://openreview.net/forum?id=TjECt9pAr4s »

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.

Author Information

Erfan Pirmorad (Toronto University)
Farnam Mansouri (University of Toronto)
Amir-massoud Farahmand (Vector Institute)

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