Efficient Spectral Control of Partially Observed Linear Dynamical Systems
- URL: http://arxiv.org/abs/2505.20943v1
- Date: Tue, 27 May 2025 09:28:10 GMT
- Title: Efficient Spectral Control of Partially Observed Linear Dynamical Systems
- Authors: Anand Brahmbhatt, Gon Buzaglo, Sofiia Druchyna, Elad Hazan,
- Abstract summary: We propose a new method for the problem of controlling linear dynamical systems under partial observation and adversarial disturbances.<n>Our new algorithm, Double Spectral Control (DSC), matches the best known regret guarantees while exponentially improving runtime complexity.
- Score: 14.023428539503433
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a new method for the problem of controlling linear dynamical systems under partial observation and adversarial disturbances. Our new algorithm, Double Spectral Control (DSC), matches the best known regret guarantees while exponentially improving runtime complexity over previous approaches in its dependence on the system's stability margin. Our key innovation is a two-level spectral approximation strategy, leveraging double convolution with a universal basis of spectral filters, enabling efficient and accurate learning of the best linear dynamical controllers.
Related papers
- A New Approach to Controlling Linear Dynamical Systems [14.023428539503433]
Our algorithm achieves a running time that scales polylogarithmically with the inverse of the stability margin.<n>The technique, which may be of independent interest, is based on a novel convex relaxation that approximates linear control policies.
arXiv Detail & Related papers (2025-04-04T21:37:46Z) - Change-Point Detection in Industrial Data Streams based on Online Dynamic Mode Decomposition with Control [5.293458740536858]
We propose a novel change-point detection method based on online Dynamic Mode Decomposition with control (ODMDwC)
Our results demonstrate that this method yields intuitive and improved detection results compared to the Singular-Value-Decomposition-based method.
arXiv Detail & Related papers (2024-07-08T14:18:33Z) - Neural Lyapunov Control for Discrete-Time Systems [30.135651803114307]
A general approach is to compute a combination of a Lyapunov function and an associated control policy.
Several methods have been proposed that represent Lyapunov functions using neural networks.
We propose the first approach for learning neural Lyapunov control in a broad class of discrete-time systems.
arXiv Detail & Related papers (2023-05-11T03:28:20Z) - Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding [21.38845517949153]
This paper presents an approach, Spectral Dynamics Embedding Control (SDEC), to optimal control for nonlinear systems.
We use an infinite-dimensional feature to linearly represent the state-action value function and exploits finite-dimensional truncation approximation for practical implementation.
arXiv Detail & Related papers (2023-04-08T04:23:46Z) - Best of Both Worlds in Online Control: Competitive Ratio and Policy
Regret [61.59646565655169]
We show that several recently proposed online control algorithms achieve the best of both worlds: sublinear regret vs. the best DAC policy selected in hindsight.
We conclude that sublinear regret vs. the optimal competitive policy is attainable when the linear dynamical system is unknown.
arXiv Detail & Related papers (2022-11-21T07:29:08Z) - Sparsity in Partially Controllable Linear Systems [56.142264865866636]
We study partially controllable linear dynamical systems specified by an underlying sparsity pattern.
Our results characterize those state variables which are irrelevant for optimal control.
arXiv Detail & Related papers (2021-10-12T16:41:47Z) - Control Occupation Kernel Regression for Nonlinear Control-Affine
Systems [6.308539010172309]
This manuscript presents an algorithm for obtaining an approximation of nonlinear high order control affine dynamical systems.
The vector valued structure of the Hilbert space allows for simultaneous approximation of the drift and control effectiveness components of the control affine system.
arXiv Detail & Related papers (2021-05-31T21:14:30Z) - Derivative-Free Policy Optimization for Risk-Sensitive and Robust
Control Design: Implicit Regularization and Sample Complexity [15.940861063732608]
Direct policy search serves as one of the workhorses in modern reinforcement learning (RL)
We investigate the convergence theory of policy robustness (PG) methods for the linear risk-sensitive and robust controller.
One feature of our algorithms is that during the learning phase, a certain level complexity/risk-sensitivity controller is preserved.
arXiv Detail & Related papers (2021-01-04T16:00:46Z) - Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
We propose a novel optimization backbone for visual SLAM systems.
We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems.
Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
arXiv Detail & Related papers (2020-11-02T18:02:26Z) - Reinforcement Learning with Fast Stabilization in Linear Dynamical
Systems [91.43582419264763]
We study model-based reinforcement learning (RL) in unknown stabilizable linear dynamical systems.
We propose an algorithm that certifies fast stabilization of the underlying system by effectively exploring the environment.
We show that the proposed algorithm attains $tildemathcalO(sqrtT)$ regret after $T$ time steps of agent-environment interaction.
arXiv Detail & Related papers (2020-07-23T23:06:40Z) - Single-step deep reinforcement learning for open-loop control of laminar
and turbulent flows [0.0]
This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems.
It combines a novel, "degenerate" version of the prototypical policy optimization (PPO) algorithm, that trains a neural network in optimizing the system only once per learning episode.
arXiv Detail & Related papers (2020-06-04T16:11:26Z) - Logarithmic Regret Bound in Partially Observable Linear Dynamical
Systems [91.43582419264763]
We study the problem of system identification and adaptive control in partially observable linear dynamical systems.
We present the first model estimation method with finite-time guarantees in both open and closed-loop system identification.
We show that AdaptOn is the first algorithm that achieves $textpolylogleft(Tright)$ regret in adaptive control of unknown partially observable linear dynamical systems.
arXiv Detail & Related papers (2020-03-25T06:00:33Z) - Adaptive Control and Regret Minimization in Linear Quadratic Gaussian
(LQG) Setting [91.43582419264763]
We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty.
LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
arXiv Detail & Related papers (2020-03-12T19:56:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.