Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
2011.11852
Cited By

Policy Optimization for Markovian Jump Linear Quadratic Control:
Gradient-Based Methods and Global Convergence

Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence

24 November 2020

Joao Paulo Jansch-Porto

Geir Dullerud

ArXiv (abs)PDF HTML

Papers citing "Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence"

4 / 4 papers shown

Convergence of Gradient-based MAML in LQR

Convergence of Gradient-based MAML in LQRIEEE Conference on Decision and Control (CDC), 2023

Geir E. Dullerud

402

8

0

12 Sep 2023

On the Optimization Landscape of Dynamic Output Feedback: A Case Study
for Linear Quadratic Regulator

On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic RegulatorIEEE Conference on Decision and Control (CDC), 2022

Jingliang Duan

270

3

0

12 Sep 2022

Global Convergence Using Policy Gradient Methods for Model-free
Markovian Jump Linear Quadratic Control

Global Convergence Using Policy Gradient Methods for Model-free Markovian Jump Linear Quadratic Control

249

8

0

30 Nov 2021

Certainty Equivalent Quadratic Control for Markov Jump Systems

Certainty Equivalent Quadratic Control for Markov Jump SystemsAmerican Control Conference (ACC), 2021

Davoud Ataee Tarzanagh

235

9

0

26 May 2021

Page 1 of 1