Mission-critical spectrum sharing with decentralized Multi-Agent Reinforcement Learning

arXiv:2610.09213 · eess.SY, cs.IT, cs.SY, math.IT, math.OC · Submitted 2026-10-06 · Read on arXiv

eess.SY, cs.IT, cs.SY, math.IT, math.OC

Submitted: 2026-10-06

Updated: 2026-10-06

Terminology

• Actor — 20× in this paper · explained in 3 episodes
• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Actor-Critic Learning — 1× in this paper · explained in 1 episode
• Deferral — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Multi-Agent Reinforcement Learning (MARL) — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning
• Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems

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