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，用于联网人工胰腺（AP）系统。尽管现有基于DRL的AP控制器通常假设周期性控制更新，但网络控制系统（NCSs）需要降低通信频率以实现节能运行，而这直接与控制更新相关。然而，同时学习胰岛素给药剂量与更新时机显著增加了学习问题的复杂性。为缓解这一复杂性，我们开发了一种实用的基于DRL的控制器设计方法，通过引入基于血糖变化定义的规则化判据，避免显式学习更新时机。这使得决策发生在不规则的间隔内，问题自然被建模为半马尔可夫决策过程（SMDP），并为此扩展了标准DRL算法。数值实验表明，所提方法在保持控制性能的同时提高了通信效率。