With the advancements in reinforcement learning (RL), new variants of this artificial
intelligence approach have been introduced in the literature. This has led to increased …

Background Reinforcement learning (RL) is a computational approach to understanding and
automating goal-directed learning and decision-making. It is designed for problems which …

Individuals with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies
need. As a result, they must continually make decisions about how much insulin to self …

Developing diagnostic and treatment solutions for medical applications is often challenging
due to the complex dynamics, partial observability, high inter-and intra-population variability …

Controlling blood glucose levels in diabetic patients is important for managing their health
and quality of life. Several algorithms based on model predictive control and reinforcement …

Background: Type 1 diabetes mellitus (T1DM) is characterized by chronic insulin deficiency
and consequent hyperglycemia. Patients with T1DM require long-term exogenous insulin …

Although reinforcement learning (RL) is suitable for highly uncertain systems, the
applicability of this class of algorithms to medical treatment may be limited by the patient …

External artificial pancreas with autonomous control algorithms has proved its effectiveness
in glucose regulation for type 1 diabetes. Nonetheless, most existing algorithms cannot …

In this paper, we test and evaluate policy gradient reinforcement learning for automated
blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown …

Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas
(AP), have been shown to optimize glucose control and reduce the self-management …