Finite Sample Analyses for Continuous-time Linear Systems: System Identification and Online Control
Keywords: online control, system identification, continuous-time linear system
Abstract: Real world evolves in continuous time but computations are done from finite samples. Therefore, we study algorithms using finite observations in continuous-time linear dynamical systems.
We first study the system identification problem, and propose a first non-asymptotic error analysis with finite observations. Our algorithm identifies system parameters without needing integrated observations over certain time intervals, making it more practical for real-world applications. Further we propose a lower bound result that shows our estimator is provably optimal up to constant factors.
Moreover, we apply the above algorithm to online control regret analysis for continuous-time linear system. Our system identification method allows us explore more efficiently, enabling the swift detection of ineffective policies. We achieve a regret of $\mathcal{O}(\sqrt{T})$ over a single $T$-time horizon in a controllable system, requiring only $\mathcal{O}(T)$ observations of the system.
Primary Area: learning theory
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Submission Number: 5369
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