Reinforcement Learning State Estimation for High-Dimensional Nonlinear Systems
In high-dimensional nonlinear systems such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) of the dynamics. However, ROMs are prone to large errors, which negatively affects the performance of the estimator. Here, we introduce the reinforcement learning reduced-order estimator (RL-ROE), a ROM-based estimator in which the data assimilation feedback term is given by a nonlinear stochastic policy trained through reinforcement learning. The flexibility of the nonlinear policy enables the RL-ROE to compensate for errors of the ROM, while still taking advantage of the imperfect knowledge of the dynamics. We show that the trained RL-ROE is able to outperform a Kalman filter designed using the same ROM, and displays robust estimation performance with respect to different reference trajectories and initial state estimates.
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