Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries

15 March 2024

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