arXiv Machine Learning September 17, 2026 By Ashok S. Kumar, Nancy Nayak, Sheetal Kalyani, Himal A. Suraweera, Lajos Hanzo

DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Efficient Resource Utilization

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reinforcement-learning

arXiv AI

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When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control

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arXiv AI

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Active Beyond-Diagonal RIS Empowered Heterogeneous Edge Computing: A Distributional Reinforcement Learning Approach

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reinforcement-learning

arXiv AI

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Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks

arXiv:2606. 28339v1 Announce Type: cross Abstract: Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments, where conventional terrestrial deployments often fail to ensure stable coverage.

By Marwan Dhuheir, Thang X. Vu, Symeon Chatzinotas

agents reinforcement-learning benchmarks

arXiv AI

Sep 15

Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

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arXiv Machine Learning

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arXiv Machine Learning

Jun 19

Utility-Aware DRL-Based TXOP Adaptation for NR-U and Wi-Fi Coexistence Networks

arXiv:2605. 00457v4 Announce Type: replace-cross Abstract: The coexistence of NR-U and Wi-Fi in the unlicensed spectrum introduces a challenging resource management problem, where heterogeneous channel access mechanisms can lead to unbalanced spectrum utilization and severe Wi-Fi performance degradation.

By Po-Heng Chou, Yi-Fang Yu, Shou-Yu Chen, Chiapin Wang

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