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Found 13 papers, 1 papers with code
Active Learning for Control-Oriented Identification of Nonlinear Systems
Model-based reinforcement learning is an effective approach for controlling an unknown system.
Rate-Optimal Non-Asymptotics for the Quadratic Prediction Error Method
We study the quadratic prediction error method -- i. e., nonlinear least squares -- for a class of time-varying parametric predictor models satisfying a certain identifiability condition.
Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
We show that whenever the topologies of $L^2$ and $\Psi_p$ are comparable on our hypothesis class $\mathscr{F}$ -- that is, $\mathscr{F}$ is a weakly sub-Gaussian class: $\|f\|_{\Psi_p} \lesssim \|f\|_{L^2}^\eta$ for some $\eta\in (0, 1]$ -- the empirical risk minimizer achieves a rate that only depends on the complexity of the class and second order statistics in its leading term.
A Tutorial on the Non-Asymptotic Theory of System Identification
This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification.
The Fundamental Limitations of Learning Linear-Quadratic Regulators
We present a local minimax lower bound on the excess cost of designing a linear-quadratic controller from offline data.
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes
We present a simple proof for bounding the smallest eigenvalue of the empirical covariance in a causal Gaussian process.
Statistical Learning Theory for Control: A Finite Sample Perspective
This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification.
Learning with little mixing
We study square loss in a realizable time-series framework with martingale difference noise.
How are policy gradient methods affected by the limits of control?
We study stochastic policy gradient methods from the perspective of control-theoretic limitations.
Learning to Control Linear Systems can be Hard
In this paper, we study the statistical difficulty of learning to control linear systems.
Single Trajectory Nonparametric Learning of Nonlinear Dynamics
Given a single trajectory of a dynamical system, we analyze the performance of the nonparametric least squares estimator (LSE).
Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems
TWe establish regret lower bounds for adaptively controlling an unknown linear Gaussian system with quadratic costs.
On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix
After defining the intrinsic notion of an uninformative optimal policy in terms of a singularity condition for Fisher information we obtain local minimax regret lower bounds for such uninformative instances of LQR by appealing to van Trees' inequality (Bayesian Cram\'er-Rao) and a representation of regret in terms of a quadratic form (Bellman error).
