3 papers
cs.NI2026
From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control
Vincenzo Norman Vitale, Mohammad Solki, Antonia Maria Tulino +2
Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is essential for NextG interactive applications, yet providing strict End-to-End (E2E) peak late…
cs.NI2026
A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications
Ozan Aygün, Vincenzo Norman Vitale, Antonia M. Tulino +3
Next-generation networks aim to provide performance guarantees to real-time interactive services that require timely and cost-efficient packet delivery. In this context, the goal i…
cs.NI2025
A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
Vincenzo Norman Vitale, Antonia Maria Tulino, Andreas F. Molisch +1
Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is increasingly essential for a range of interactive applications, such as industrial automation…