Papers › Safely Learning to Control the Constrained Linear Quadratic Regulator
Safely Learning to Control the Constrained Linear Quadratic Regulator
Sarah Dean, Stephen Tu, Nikolai Matni, Benjamin Recht
We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a framework which allows for system identification through persistent excitation, while maintaining safety by guaranteeing the satisfaction of state and input constraints. This framework involves a novel method for synthesizing robust constraint-satisfying feedback controllers, leveraging newly developed tools from system level synthesis. We connect statistical results with cost sub-optimality bounds to give non-asymptotic guarantees on both estimation and controller performance.
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