Author

Daniel F. Macedo

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#artificial intelligence Preprint Oct 2026

Deadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks

Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles. However, existing approaches often overlook network contention among co-located services with heterogeneous and dynamic latency requirements. While time-sensitive networking (TSN) pro...

Bernardo A. C. Pereira, Marcos Carvalho, Fatih Temiz et al. · 0 citations

Preprint Aug 2026

Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks

The multi-agent transformer (MAT) is adopted to model inter-queue dependencies via attention over agents' observations and actions, enabling implicit coordination across heterogeneous co-located XR applications and results show that the proposed method outperforms baselines.

Marcos Carvalho, Fatih Temiz, Shavbo Salehi et al. · 0 citations

Preprint Aug 2026

Multi-Agent Reinforcement Learning for Online Traffic Scheduling in Time-Sensitive Application

This paper proposes a multi-agent reinforcement learning (MARL) framework for TSN scheduling, where each TSN queue is modeled as an autonomous agent and the Heterogeneous-Agent Proximal Policy Optimization (HAPPO) algorithm is employed to explicitly model inter-agent dependencies and jointly optimize service delivery a...

Marcos Carvalho, Fatih Temiz, Shavbo Salehi et al. · 0 citations