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20172026
most citedOnline Learning with Inexact Proximal Online Gradient Descent Algorithms

111 citations · 420 across the 100 of their papers we have counts for

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Showing 2022Show all

12 papers · 1 filter

cs.DC2022★ 3 cited

SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

Marco Bornstein, Tahseen Rabbani, Evan Wang +2

The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train…

cs.RO2022★ 1 cited

DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments

Aakriti Agrawal, Senthil Hariharan, Amrit Singh Bedi +1

We present a novel reinforcement learning (RL) based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments. Our approach is designed fo…

cs.RO2022

DMCA: Dense Multi-agent Navigation using Attention and Communication

Senthil Hariharan Arul, Amrit Singh Bedi, Dinesh Manocha

In decentralized multi-robot navigation, ensuring safe and efficient movement with limited environmental awareness remains a challenge. While robots traditionally navigate based on…

cs.RO2022★ 1 cited

RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments

Aakriti Agrawal, Amrit Singh Bedi, Dinesh Manocha

We present a novel reinforcement learning based algorithm for multi-robot task allocation problem in warehouse environments. We formulate it as a Markov Decision Process and solve…

cs.RO2022★ 2 cited

HTRON:Efficient Outdoor Navigation with Sparse Rewards via Heavy Tailed Adaptive Reinforce Algorithm

Kasun Weerakoon, Souradip Chakraborty, Nare Karapetyan +3

We present a novel approach to improve the performance of deep reinforcement learning (DRL) based outdoor robot navigation systems. Most, existing DRL methods are based on carefull…

cs.LG2022★ 4 cited

FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi +3

Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy d…