activity
20182022
most citedStabilizing Dynamical Systems via Policy Gradient Methods

13 citations · 24 across the 5 of their papers we have counts for

collaborators

7 papers

math.OC20225 cited

Globally Convergent Policy Search over Dynamic Filters for Output Estimation

Jack Umenberger, Max Simchowitz, Juan C. Perdomo +2

We introduce the first direct policy search algorithm which provably converges to the globally optimal filter for the classical problem of predicting the outputs…

eess.SY202113 cited

Stabilizing Dynamical Systems via Policy Gradient Methods

Juan C. Perdomo, Jack Umenberger, Max Simchowitz

Stabilizing an unknown control system is one of the most fundamental problems in control systems engineering. In this paper, we provide a simple, model-free algorithm for stabilizi…

eess.SY2021

Distributed Identification of Contracting and/or Monotone Network Dynamics

Max Revay, Jack Umenberger, Ian R. Manchester

This paper proposes methods for identification of large-scale networked systems with guarantees that the resulting model will be contracting -- a strong form of nonlinear stability…

math.OC20192 cited

Optimistic robust linear quadratic dual control

Jack Umenberger, Thomas B. Schon

Recent work by Mania et al. has proved that certainty equivalent control achieves nearly optimal regret for linear systems with quadratic costs. However, when parameter uncertainty…

math.OC20194 cited

Robust exploration in linear quadratic reinforcement learning

Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön +1

This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex opti…

math.OC2018

Inverse Quadratic Optimal Control for Discrete-Time Linear Systems

Han Zhang, Jack Umenberger, Xiaoming Hu

In this paper, we consider the inverse optimal control problem for the discrete-time linear quadratic regulator, over finite-time horizons. Given observations of the optimal trajec…