most citedDistributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control

71 citations · 72 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NI20221 cited

Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach

Francesco Pase, Marco Giordani, Giampaolo Cuozzo +4

This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks. As far as th…

cs.LG2022

Rate-Constrained Remote Contextual Bandits

Francesco Pase, Deniz Gündüz, Michele Zorzi

We consider a rate-constrained contextual multi-armed bandit (RC-CMAB) problem, in which a group of agents are solving the same contextual multi-armed bandit (CMAB) problem. Howeve…

cs.IT2022

Remote Contextual Bandits

Francesco Pase, Deniz Gunduz, Michele Zorzi

We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by…

cs.LG2021

On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI

Francesco Pase, Marco Giordani, Michele Zorzi

Federated learning (FL) has recently emerged as an attractive decentralized solution for wireless networks to collaboratively train a shared model while keeping data localized. As…

cs.LG202171 cited

Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control

Federico Venturini, Federico Mason, Francesco Pase +4

Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to the price redu…