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.

In this material, we first describe the importance of action validity prediction networks. Then, we introduce the details of the benchmarks, provide the model architecture, and present the additional experimental results, which are missing in the main article. We present the results of wall-clock time for computing the ground-truth action validity in Figure s.1. It shows that computing the action validity for a combination of 100 bricks needs more than 20 seconds. Moreover, we summarize the comparisons between possible action validation approaches as shown in Table s.1.0