Abstract
Data assimilation (DA) plays a pivotal role in diverse applications,
ranging from climate predictions and weather forecasts to trajectory
planning for autonomous vehicles. A prime example is the widely used
ensemble Kalman filter (EnKF), which relies on linear updates to
minimize variance among the ensemble of forecast states. Recent
advancements have seen the emergence of deep learning approaches in this
domain, primarily within a supervised learning framework. However, the
adaptability of such models to untrained scenarios remains a challenge.
In this study, we introduce a novel DA strategy that utilizes
reinforcement learning (RL) to apply state corrections using full or
partial observations of the state variables. Our investigation focuses
on demonstrating this approach to the chaotic Lorenz ’63 system, where
the agent’s objective is to minimize the root-mean-squared error between
the observations and corresponding forecast states. Consequently, the
agent develops a correction strategy, enhancing model forecasts based on
available system state observations. Our strategy employs a stochastic
action policy, enabling a Monte Carlo-based DA framework that relies on
randomly sampling the policy to generate an ensemble of assimilated
realizations. Results demonstrate that the developed RL algorithm
performs favorably when compared to the EnKF. Additionally, we
illustrate the agent’s capability to assimilate non-Gaussian data,
addressing a significant limitation of the EnKF.