The goal of many applications in energy and transport sectors is to control
turbulent flows. However, because of chaotic dynamics and high dimensionality,
the control of turbulent flows is exceedingly difficult. Model-free
reinforcement learning (RL) methods can discover optimal control policies by
interacting with the environment, but they require full state information,
which is often unavailable in experimental settings. We propose a
data-assimilated model-based RL (DA-MBRL) framework for systems with partial
observability and noisy measurements. Our framework employs a control-aware
Echo State Network for data-driven prediction of the dynamics, and integrates
data assimilation with an Ensemble Kalman Filter for real-time state
estimation. An off-policy actor-critic algorithm is employed to learn optimal
control strategies from state estimates. The framework is tested on the
Kuramoto-Sivashinsky equation, demonstrating its effectiveness in stabilizing a
spatiotemporally chaotic flow from noisy and partial measurements.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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