54 citations · 217 across the 13 of their papers we have counts for
6 papers · 1 filter
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint
Stephen Tu, Benjamin Recht
The effectiveness of model-based versus model-free methods is a long-standing question in reinforcement learning (RL). Motivated by recent empirical success of RL on continuous con…
Minimax Lower Bounds for -Norm Estimation
Stephen Tu, Ross Boczar, Benjamin Recht
The problem of estimating the -norm of an LTI system from noisy input/output measurements has attracted recent attention as an alternative to parameter identifi…
Safely Learning to Control the Constrained Linear Quadratic Regulator
Sarah Dean, Stephen Tu, Nikolai Matni +1
We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a fra…
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
Sarah Dean, Horia Mania, Nikolai Matni +2
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…
Learning Contracting Vector Fields For Stable Imitation Learning
Vikas Sindhwani, Stephen Tu, Mohi Khansari
We propose a new non-parametric framework for learning incrementally stable dynamical systems x' = f(x) from a set of sampled trajectories. We construct a rich family of smooth vec…
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification
Max Simchowitz, Horia Mania, Stephen Tu +2
We prove that the ordinary least-squares (OLS) estimator attains nearly minimax optimal performance for the identification of linear dynamical systems from a single observed trajec…