Learning Linear-Quadratic Regulators Efficiently with only √(T) Regret

02/17/2019
by   Alon Cohen, et al.
0

We present the first computationally-efficient algorithm with O(√(T)) regret for learning in Linear Quadratic Control systems with unknown dynamics. By that, we resolve an open question of Abbasi-Yadkori and Szepesvári (2011) and Dean, Mania, Matni, Recht, and Tu (2018).

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

01/31/2020

Regret Minimization in Partially Observable Linear Quadratic Control

We study the problem of regret minimization in partially observable line...
07/13/2020

Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation

We study the exploration-exploitation dilemma in the linear quadratic re...
06/19/2020

Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory

We present the first approach for learning – from a single trajectory – ...
11/03/2020

Episodic Linear Quadratic Regulators with Low-rank Transitions

Linear Quadratic Regulators (LQR) achieve enormous successful real-world...
02/19/2020

Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently

We consider the problem of learning in Linear Quadratic Control systems ...
05/23/2018

Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator

We consider adaptive control of the Linear Quadratic Regulator (LQR), wh...
12/09/2019

Optimism in Reinforcement Learning with Generalized Linear Function Approximation

We design a new provably efficient algorithm for episodic reinforcement ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.