ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2401.14534
  4. Cited By
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML
  Approach for Model-free LQR

Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR

25 January 2024
Leonardo F. Toso
Donglin Zhan
James Anderson
Han Wang
ArXivPDFHTML

Papers citing "Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR"

4 / 4 papers shown
Title
Learning Stabilizing Policies via an Unstable Subspace Representation
Learning Stabilizing Policies via an Unstable Subspace Representation
Leonardo F. Toso
Lintao Ye
James Anderson
19
0
0
02 May 2025
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
Donglin Zhan
Leonardo F. Toso
James Anderson
85
1
0
04 Feb 2025
Stabilizing Dynamical Systems via Policy Gradient Methods
Stabilizing Dynamical Systems via Policy Gradient Methods
Juan C. Perdomo
Jack Umenberger
Max Simchowitz
13
44
0
13 Oct 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
237
11,568
0
09 Mar 2017
1