Distributed Online Learning for Time-Critical Communication in 6G Industrial Subnetworks

arXiv:2605.06437 · eess.SY, cs.SY · Submitted 2026-05-07 · Read on arXiv

eess.SY, cs.SY

Submitted: 2026-05-07

Updated: 2026-10-16

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning (DRL) — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Related papers

• One Request, Multiple Experts: LLM Orchestrates Domain Specific Models via Adaptive Task Routing
• A Geometric Decision Procedure for STL Feasibility and Repair
• Scaling up statistical model checking of cyber-physical systems via algorithm ensemble and parallel simulations over HPC infrastructures
• From Asymptotic to Designer-Assigned-Time Control: A Review of Stability Notions, Design Mechanisms, and Controller Architectures
• Battery Second Life: A Review of Experimental Studies
• Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems