Search Results for author: Ingvar Ziemann

Found 6 papers, 0 papers with code

Learning with little mixing

no code implementations16 Jun 2022 Ingvar Ziemann, Stephen Tu

We study square loss in a realizable time-series framework with martingale difference noise.

Time Series

How are policy gradient methods affected by the limits of control?

no code implementations14 Jun 2022 Ingvar Ziemann, Anastasios Tsiamis, Henrik Sandberg, Nikolai Matni

We study stochastic policy gradient methods from the perspective of control-theoretic limitations.

Policy Gradient Methods

Learning to Control Linear Systems can be Hard

no code implementations27 May 2022 Anastasios Tsiamis, Ingvar Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas

In this paper, we study the statistical difficulty of learning to control linear systems.

online learning

Single Trajectory Nonparametric Learning of Nonlinear Dynamics

no code implementations16 Feb 2022 Ingvar Ziemann, Henrik Sandberg, Nikolai Matni

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

no code implementations5 Jan 2022 Ingvar Ziemann, Henrik Sandberg

This paper presents local minimax regret lower bounds for adaptively controlling linear-quadratic-Gaussian (LQG) systems.

On Uninformative Optimal Policies in Adaptive LQR with Unknown B-Matrix

no code implementations18 Nov 2020 Ingvar Ziemann, Henrik Sandberg

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).

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