Online greedy identification of linear dynamical systems
Not on the map. Why?
Abstract (arXiv)
This work addresses the problem of exploration in an unknown environment. For linear dynamical systems, we use an experimental design framework and introduce an online greedy policy where the control maximizes the information of the next step. In a setting with a limited number of experimental trials, our algorithm has low complexity and shows experimentally competitive performances compared to more elaborate gradient-based methods.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 13 Apr 2022.
Builds on
7 of 29 references.
References · 7
- 2018Learning Without Mixing: Towards A Sharp Analysis of Linear System Identificationcited by 158
- 2018Near optimal finite time identification of arbitrary linear dynamical systemscited by 89
- 2020Active Learning for Nonlinear System Identification with Guaranteescited by 78
- 2019Finite Sample Analysis of Stochastic System Identificationcited by 67
- 2020Active Learning for Identification of Linear Dynamical Systemscited by 33
- 2020Finite-time Identification of Stable Linear Systems: Optimality of the Least-Squares Estimatorcited by 26
- 2021Task-Optimal Exploration in Linear Dynamical Systemscited by 15
Built on by
Citing papers per year: 2023: 1.
1 paper.