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Learning controllers from trajectories corrupted by process and measurement noise is difficult when the system matrices and the noise covariances are unknown: noisy regressors bias least squares, and model uncertainty ca...
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Building on the uncertainty description developed in Part~I, this paper introduces a Fisher-majorized domain-randomized generalized policy iteration framework for data-driven linear quadratic regulator (LQR) synthesis. A...