Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
2403.09940
Cited By
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
15 March 2024
Swetha Ganesh
Jiayu Chen
Gugan Thoppe
Vaneet Aggarwal
FedML
Re-assign community
ArXiv
PDF
HTML
Papers citing
"Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries"
7 / 7 papers shown
Title
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Guangchen Lan
Dong-Jun Han
Abolfazl Hashemi
Vaneet Aggarwal
Christopher G. Brinton
122
15
0
09 Apr 2024
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function
Saeed Masiha
Saber Salehkaleybar
Niao He
Negar Kiyavash
Patrick Thiran
79
18
0
25 May 2022
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums
Sadegh Farhadkhani
R. Guerraoui
Nirupam Gupta
Rafael Pinot
John Stephan
FedML
29
67
0
24 May 2022
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach
Qinbo Bai
Amrit Singh Bedi
Mridul Agarwal
Alec Koppel
Vaneet Aggarwal
99
56
0
13 Sep 2021
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach
Trevor Bonjour
Marina Haliem
A. Alsalem
Shilpa Thomas
Hongyu Li
Vaneet Aggarwal
M. Kejriwal
Bharat K. Bhargava
27
14
0
01 Mar 2021
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
Junyu Zhang
Chengzhuo Ni
Zheng Yu
Csaba Szepesvári
Mengdi Wang
44
66
0
17 Feb 2021
Deep Reinforcement Learning for Autonomous Driving: A Survey
B. R. Kiran
Ibrahim Sobh
V. Talpaert
Patrick Mannion
A. A. Sallab
S. Yogamani
P. Pérez
143
1,624
0
02 Feb 2020
1