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- W4289490601 abstract "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." @default.
- W4289490601 created "2022-08-02" @default.
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- W4289490601 date "2018-09-26" @default.
- W4289490601 modified "2023-10-16" @default.
- W4289490601 title "Safely Learning to Control the Constrained Linear Quadratic Regulator" @default.
- W4289490601 doi "https://doi.org/10.48550/arxiv.1809.10121" @default.
- W4289490601 hasPublicationYear "2018" @default.
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