118 citations · 158 across the 6 of their papers we have counts for
6 papers
Learning to Coordinate via Quantum Entanglement in Multi-Agent Reinforcement Learning
John Gardiner, Orlando Romero, Brendan Tivnan +2
The inability to communicate poses a major challenge to coordination in multi-agent reinforcement learning (MARL). Prior work has explored correlating local policies via shared ran…
Long-Term Mapping of the Douro River Plume with Multi-Agent Reinforcement Learning
Nicolò Dal Fabbro, Milad Mesbahi, Renato Mendes +2
We study the problem of long-term (multiple days) mapping of a river plume using multiple autonomous underwater vehicles (AUVs), focusing on the Douro river representative use-case…
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Nicolò Dal Fabbro, Michele Rossi, Luca Schenato +1
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such a…
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels
Francesca Meneghello, Nicolò Dal Fabbro, Domenico Garlisi +2
In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive…
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
Nicolò Dal Fabbro, Subhrakanti Dey, Michele Rossi +1
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scena…
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points
Francesca Meneghello, Domenico Garlisi, Nicolò Dal Fabbro +2
In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the po…