most citedMulti-Agent Reinforcement Learning for Pragmatic Communication and Control

3 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2024

Using Deep Reinforcement Learning to Enhance Channel Sampling Patterns in Integrated Sensing and Communication

Federico Mason, Jacopo Pegoraro

In Integrated Sensing And Communication (ISAC) systems, estimating the micro-Doppler (mD) spectrogram of a target requires combining channel estimates retrieved from communication…

eess.SY2024

Push- and Pull-based Effective Communication in Cyber-Physical Systems

Pietro Talli, Federico Mason, Federico Chiariotti +1

In Cyber Physical Systems (CPSs), two groups of actors interact toward the maximization of system performance: the sensors, observing and disseminating the system state, and the ac…

cs.NI2023

Fast Context Adaptation in Cost-Aware Continual Learning

Seyyidahmed Lahmer, Federico Mason, Federico Chiariotti +1

In the past few years, DRL has become a valuable solution to automatically learn efficient resource management strategies in complex networks with time-varying statistics. However,…

cs.RO20233 cited

Multi-Agent Reinforcement Learning for Pragmatic Communication and Control

Federico Mason, Federico Chiariotti, Andrea Zanella +1

The automation of factories and manufacturing processes has been accelerating over the past few years, boosted by the Industry 4.0 paradigm, including diverse scenarios with mobile…

cs.NI20233 cited

Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks

Filippo Bragato, Tommaso Lotta, Gianmaria Ventura +4

To ensure safety in teleoperated driving scenarios, communication between vehicles and remote drivers must satisfy strict latency and reliability requirements. In this context, Pre…