使用深度确定性策略梯度学习面向高优先级用户的资源调度
Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients
AI总结：
本文研究面向高优先级用户的通信资源调度问题，采用可输出连续值的 DDPG 学习调度策略，并经轻量级后处理在灵活总效用目标上取得高性能。
摘要：
移动通信能力的进步为院前与院内护理流程的更紧密整合打开了大门。例如，医疗专家可以获得能力来指导现场急救人员，并且反过来能够接收实时生命体征或视觉画面。将此类性能关键型应用与移动通信高度复杂的运行机制相结合，需要既可靠又高效、同时易于与现有系统集成的解决方案。本文探索应用 Deep Deterministic Policy Gradient（DDPG，深度确定性策略梯度）方法来学习一种通信资源调度算法，并特别关注优先级用户。与流行的 Deep-Q-Network 方法不同，DDPG 能够产生连续值输出。通过轻量级后处理，所得调度器能够在灵活的总效用目标上实现高性能。
英文摘要：
Advances in mobile communication capabilities open the door for closer integration of pre-hospital and in-hospital care processes. For example, medical specialists can be enabled to guide on-site paramedics and can, in turn, be supplied with live vitals or visuals. Consolidating such performance-critical applications with the highly complex workings of mobile communications requires solutions both reliable and efficient, yet easy to integrate with existing systems. This paper explores the application of Deep Deterministic Policy Gradient~(\ddpg) methods for learning a communications resource scheduling algorithm with special regards to priority users. Unlike the popular Deep-Q-Network methods, the \ddpg is able to produce continuous-valued output. With light post-processing, the resulting scheduler is able to achieve high performance on a flexible sum-utility goal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
