Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations
This work addresses control of complex dynamical systems like fluid flows, which is incremental as it adapts existing RL methods to SPDEs.
The authors tackled the problem of controlling stochastic partial differential equations (SPDEs) by formulating it as a reinforcement learning task and applying a deep deterministic policy gradient method, achieving performance tested on the stochastic Burgers' equation for turbulent fluid flow.
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.