arXiv research

The paper studies optimal transport in linear quadratic systems and derives interpolation inequalities.

Efficient algorithm reduces control system learning regret to sqrt(T).

LqgOpt learns optimal control in unknown LQG systems with minimal regret.

Safe control of systems with unknown dynamics using persistent excitation.

Policy gradient converges to globally optimal policy in nearly linear-quadratic systems.

Study derivative-free methods for linear policies in linear-quadratic systems.

Algorithm reduces regret in partially observable systems by learning dynamics and using optimistic control.

Study shows certainty equivalent policy minimizes regret in continuous-time systems.

New algorithms achieve logarithmic regret in learning linear quadratic control systems.

Develops a control framework for systemic risk under uncertainty.

Study optimizes resource allocation in noisy systems for better control.

Paper uses Koopman operator and Nyström method for efficient nonlinear control.

Certainty equivalent controllers perform nearly optimally in LQ control problems with unknown dynamics.

New model-free algorithm achieves similar LQR regret guarantees.

Policy gradient methods find Nash equilibrium in noisy games.

New algorithm learns LQR with $O(\sqrt{T})$ regret using Langevin dynamics and excitation.

This paper studies how gradient descent in control systems can perform well on unseen data.

We consider a general time-inconsistent stochastic linear-quadratic differential game. The time-inconsistency arises from the presence of quadratic terms of the expected state as well as state-dependent term in the objective functionals. We define an equilibrium strategy, which is different from the classical one, and …

New bounds for adaptive control in high dimensions without fixed state space.

Paper solves time-inconsistent control problems with BSDEs.

We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown linear system is controlled subject to quadratic costs. Leveraging recent developments in the estimation of linear systems and in robust controller synthesis, we present the first provably polynomial time algorithm that provides high…

TSAC achieves optimal frequentist regret in adaptive control of LQRs.

The main challenge for adaptive regulation of linear-quadratic systems is the trade-off between identification and control. An adaptive policy needs to address both the estimation of unknown dynamics parameters (exploration), as well as the regulation of the underlying system (exploitation). To this end, optimism-based…

Global convergence proved for multi-agent LQRs with hierarchical actor-critic.

Policy gradient methods converge for LQR problems with noisy state dynamics.

Efficiently solves exploration-exploitation in LQR using Lagrangian relaxation.

Safe RL-based vibration control using LQR guidance.

Optimal attack against autoregressive models by manipulating environment states.

This paper studies a class of continuous-time scalar-state stochastic Linear-Quadratic (LQ) optimal control problem with the linear control constraints. Applying the state separation theorem induced from its special structure, we develop the explicit solution for this class of problem. The revealed optimal control poli…

This paper addresses the optimal control problem known as the Linear Quadratic Regulator in the case when the dynamics are unknown. We propose a multi-stage procedure, called Coarse-ID control, that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then d…

Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. They are appealing as they are general purpose and easy to implement; however, they also come with fewer theoretical guarantees than model-bas…

New algorithm achieves logarithmic regret for adversarial online control.

We study the problem of adaptive control of a high dimensional linear quadratic (LQ) system. Previous work established the asymptotic convergence to an optimal controller for various adaptive control schemes. More recently, for the average cost LQ problem, a regret bound of ${O}(\sqrt{T})$ was shown, apart form logarit…

Regularized least-squares approaches have been successfully applied to linear system identification. Recent approaches use quadratic penalty terms on the unknown impulse response defined by stable spline kernels, which control model space complexity by leveraging regularity and bounded-input bounded-output stability. T…

Study policy gradient for large-agent mean-field control and game in continuous time.

New algorithm reduces learning regret in multi-agent systems with unknown dynamics.

Study shows a linear quadratic regulator's imitation learning converges globally.

Solves steering problem with continuous time, Hilbert-Schmidt cost, and matrix ODEs.

We propose a simple model of the banking system incorporating a game feature where the evolution of monetary reserve is modeled as a system of coupled Feller diffusions. The Markov Nash equilibrium generated through minimizing the linear quadratic cost subject to Cox-Ingersoll-Ross type processes creates liquidity and …

We study the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learning algorithms in this setting that guarantee $O(\sqrt{T})$ regret under mild assumptions, where $T$ is the time horizon. Our algorithms rely …

Novel approach to Nash equilibrium in mean-field stochastic games with operator resolvents.

Study task-guided exploration in linear dynamical systems, improving sample complexity.

Optimal control strategy uses random noise to adaptively control systems with unknown parameters.

Survey explores geometric aspects of policy optimization in control systems.

RL solves discrete LQ control with Gaussian optimal policy.

Logarithmic regret achieved in continuous-time linear-quadratic reinforcement learning.

Study learns state representations from observations for control, proving guarantees.

New algorithm minimizes regret in unknown LQ control systems.