Reinforcement Learning for Automated Insulin Delivery: A Comprehensive Review of Methods, Challenges, and Future Directions

Abstract

Type 1 Diabetes Mellitus (T1DM) is a chronic autoimmune disease characterized by the destruction of pancreatic β cells, resulting in complete insulin deficiency and an exogenous insulin requirement. However, despite the technological progress, classical insulin therapies and control algorithms such as Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC) are not able to follow unpredictable glucose variations due to meals, exercise, and stress. Automated Insulin Delivery (AID) systems combine continuous glucose monitoring, insulin pumps, and algorithmic decision-making but remain limited by model dependency, computational load, and lack of personalization. Reinforcement Learning (RL) has emerged as a transformative paradigm for AI systems by modeling insulin regulation as a sequential decision-making process that learns optimal dosing policies from experience. Model-free RL algorithms like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) have demonstrated improved glycemic control, whereas model-based and hybrid RL algorithms have demonstrated increased safety and sample efficiency by integrating physiological constraints. Deep Reinforcement Learning (DRL) further increases adaptability through neural network-based personalization over different patient profiles. RL can be integrated with wearable technologies, Internet of Things (IoT) ecosystems, and digital twin simulations for continuous learning, remote monitoring, and patient specific optimization. Early clinical studies show improved time-in-range (TIR), reduced hypoglycemia, and improved long-term glucose control compared to traditional methods. However, there are still several challenges, including lack of real-world data, safety in online learning, interpretability, and regulatory compliance. Future advances will need to include the development of explainable and federated RL frameworks, lightweight models for wearable deployment, and multi-hormone control systems. In summary, RL-based AID systems represent an important step towards fully autonomous and personalized artificial pancreas solutions. Their clinical success will be dependent on multidisciplinary collaboration between AI researchers, clinicians, and regulators to ensure safety, transparency, and reliability in next generation diabetes management.