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.

翻译：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，医疗专家可以指导现场急救人员，并反过来接收实时生命体征或视觉信息。将此类性能关键型应用与高度复杂的移动通信机制相结合，需要既可靠高效又易于与现有系统集成的解决方案。本文探索了深度确定性策略梯度（Deep Deterministic Policy Gradient，简称DDPG）方法在考虑优先级用户情况下学习通信资源调度算法的应用。与流行的深度Q网络方法不同，DDPG能够产生连续值输出。通过轻量后处理，所得调度器能够在灵活的总效用目标上实现高性能。