This paper proposes a deep reinforcement learning (DRL)-based event-triggered controller design for networked artificial pancreas (AP) systems. Although existing DRL-based AP controllers typically assume periodic control updates, networked control systems (NCSs) require a reduction in communication frequency to achieve energy-efficient operation, which is directly tied to control updates. However, jointly learning both insulin dosing and update timing significantly increases the complexity of the learning problem. To alleviate this complexity, we develop a practical DRL-based controller design that avoids explicitly learning update timing by introducing a rule-based criterion defined by changes in blood glucose. As a result, decision-making occurs at irregular intervals, and the problem is naturally formulated as a semi-Markov decision process (SMDP), for which we extend a standard DRL algorithm. Numerical experiments demonstrate that the proposed method improves communication efficiency while maintaining control performance.

翻译：本文提出了一种基于深度强化学习（DRL）的事件触发控制器设计方法，用于网络化人工胰腺（AP）系统。尽管现有的基于DRL的AP控制器通常采用周期性控制更新，但网络化控制系统（NCSs）需要降低通信频率以实现能效运行，这直接与控制更新相关。然而，联合学习胰岛素剂量与更新时序会显著增加学习问题的复杂性。为缓解这一复杂性，我们开发了一种实用的基于DRL的控制器设计，通过引入由血糖变化定义的规则化准则，避免显式学习更新时序。由此，决策在不规则的时间间隔内发生，问题自然被建模为半马尔可夫决策过程（SMDP），并针对该过程扩展了标准DRL算法。数值实验表明，所提方法在保持控制性能的同时提升了通信效率。