most citedGlobal Convergence of Policy Gradient for Sequential Zero-Sum Linear Quadratic Dynamic Games

26 citations · 40 across the 4 of their papers we have counts for

collaborators

5 papers

math.OC20203 cited

A Note on Nesterov's Accelerated Method in Nonconvex Optimization: a Weak Estimate Sequence Approach

Jingjing Bu, Mehran Mesbahi

We present a variant of accelerated gradient descent algorithms, adapted from Nesterov's optimal first-order methods, for weakly-quasi-convex and weakly-quasi-strongly-convex funct…

eess.SY202011 cited

Policy Gradient-based Algorithms for Continuous-time Linear Quadratic Control

Jingjing Bu, Afshin Mesbahi, Mehran Mesbahi

We consider the continuous-time Linear-Quadratic-Regulator (LQR) problem in terms of optimizing a real-valued matrix function over the set of feedback gains. The results developed…

math.OC2020

Global Convergence of Policy Gradient Algorithms for Indefinite Least Squares Stationary Optimal Control

Jingjing Bu, Mehran Mesbahi

We consider policy gradient algorithms for the indefinite least squares stationary optimal control, e.g., linear-quadratic-regulator (LQR) with indefinite state and input penalizat…

eess.SY201926 cited

Global Convergence of Policy Gradient for Sequential Zero-Sum Linear Quadratic Dynamic Games

Jingjing Bu, Lillian J. Ratliff, Mehran Mesbahi

We propose projection-free sequential algorithms for linear-quadratic dynamics games. These policy gradient based algorithms are akin to Stackelberg leadership model and can be ext…

eess.SY2019

LQR through the Lens of First Order Methods: Discrete-time Case

Jingjing Bu, Afshin Mesbahi, Maryam Fazel +1

We consider the Linear-Quadratic-Regulator (LQR) problem in terms of optimizing a real-valued matrix function over the set of feedback gains. Such a setup facilitates examining the…