Data-assimilated model-informed reinforcement learning

TL;DR

This paper introduces DA-MIRL, a novel framework combining low-order models, data assimilation, and reinforcement learning to control chaotic systems with partial and noisy observations, demonstrated on the Kuramoto-Sivashinsky equation.

Contribution

The paper develops a new integrated approach, DA-MIRL, that enables real-time control of chaotic systems using partial data and approximate models, advancing model-based RL techniques.

Findings

DA-MIRL effectively estimates the full state from partial observations.

It successfully suppresses chaos in the Kuramoto-Sivashinsky system.

The method adapts to noisy and incomplete data in real time.

Abstract

The control of spatio-temporally chaos is challenging because of high dimensionality and unpredictability. Model-free reinforcement learning (RL) discovers optimal control policies by interacting with the system, typically requiring observations of the full physical state. In practice, sensors often provide only partial and noisy measurements (observations) of the system. The objective of this paper is to develop a framework that enables the control of chaotic systems with partial and noisy observability. The proposed method, data-assimilated model-informed reinforcement learning (DA-MIRL), integrates (i) low-order models to approximate high-dimensional dynamics; (ii) sequential data assimilation to correct the model prediction when observations become available; and (iii) an off-policy actor-critic RL algorithm to adaptively learn an optimal control strategy based on the corrected state estimates. We test DA-MIRL on the spatiotemporally chaotic solutions of the Kuramoto-Sivashinsky equation. We estimate the full state of the environment with (i) a physics-based model, here, a coarse-grained model; and (ii) a data-driven model, here, the control-aware echo state network, which is proposed in this paper. We show that DA-MIRL successfully estimates and suppresses the chaotic dynamics of the environment in real time from partial observations and approximate models. This work opens opportunities for the control of partially observable chaotic systems.