Author

Daniel F. Macedo

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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 across queues.

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