# Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems

arXiv: https://arxiv.org/abs/2604.26126 · HTML: https://mlsanity.com/paper/2604.26126/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Spiking and neuromorphic RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Placed in

- Deep reinforcement learning methods / [Spiking and neuromorphic RL](https://mlsanity.com/deeprl/spiking-and-neuromorphic-rl/)

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study](https://mlsanity.com/paper/2006.05990/) (2020)
- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Deep Reinforcement Learning for Closed-Loop Blood Glucose Control](https://mlsanity.com/paper/2009.09051/) (2020)
- [Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification](https://mlsanity.com/paper/2502.02133/) (2025)
- [Learning Event-triggered Control from Data through Joint Optimization](https://mlsanity.com/paper/2008.04712/) (2020)
- [A dual mode adaptive basal-bolus advisor based on reinforcement learning](https://mlsanity.com/paper/1901.01816/) (2019)
- [Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model](https://mlsanity.com/paper/2108.06161/) (2021)
- [A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation](https://mlsanity.com/paper/2205.07070/) (2022)
- [Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control](https://mlsanity.com/paper/2305.08723/) (2023)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Bipedal Locomotion](https://mlsanity.com/paper/2511.06465/) (2025)

## Abstract (arXiv)

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.