发表机构
Idaho National Laboratory; Howard University; University of Florida（爱达荷国家实验室; 霍华德大学; 佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对关键任务频谱共享中的冲突问题，提出一种基于马尔可夫势博弈的去中心化多智能体强化学习模型，采用轻量级线性演员-评论家算法，在保持吞吐量的同时将总体冲突减少高达96.8%。
AI 中文摘要
受新兴的关键任务应用和日益拥挤的频谱的启发，我们开发了一种用于动态频谱访问的去中心化多智能体强化学习（MARL）模型。该模型使次级用户能够在共享频段上学习有效的传输策略，同时最小化与高优先级主用户以及彼此之间的冲突。我们按照马尔可夫势博弈方法设计智能体级学习器，将独立的局部更新与系统级改进联系起来。我们使用适用于资源受限边缘设备的轻量级线性演员-评论家学习器来实现这一设计，而不是计算密集型的集中式或深度多智能体架构。在具有不同现有活动的频谱环境中，学习到的策略会调整其传输策略和等待行为，以保持吞吐量，同时相对于随机和预测感知的启发式基线大幅减少传输冲突。结果证明了去中心化MARL的价值，并显示总体冲突减少了高达96.8%。
英文摘要
Motivated by emerging mission-critical applications and an increasingly congested spectrum, we develop a decentralized multi-agent reinforcement learning (MARL) model for dynamic spectrum access. The model enables secondary users to learn effective transmission strategies across shared frequency bands while minimizing collisions with high-priority primary users and among themselves. We design the agent-level learners following a Markov potential game approach, connecting independent local updates to system-level improvement. We instantiate this design using lightweight linear actor-critic learners suitable for resource-constrained edge devices, rather than computationally intensive centralized or deep multi-agent architectures. Across spectrum environments with different incumbent activities, the learned policies adapt their transmission policy and waiting behavior to preserve throughput while greatly reducing transmission collisions relative to random and forecast-aware heuristic baselines. The results establish the value of decentralized MARL and shows up to 96.8% reduction in overall collisions.
CommentsAccepted for publication in the proceedings of IEEE CCNC 2027
