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This paper presents a novel multi-agent reinforcement learning (RL) approachfor personalized glucose control in individuals with type 1 diabetes (T1D). Themethod employs a closed-loop system consisting of a blood glucose (BG)metabolic model and a multi-agent soft actor-critic RL model acting as thebasal-bolus advisor. Performance evaluation is conducted in three scenarios,comparing the RL agents to conventional therapy. Evaluation metrics includeglucose levels (minimum, maximum, and mean), time spent in different BG ranges,and average daily bolus and basal insulin dosages. Results demonstrate that theRL-based basal-bolus advisor significantly improves glucose control, reducingglycemic variability and increasing time spent within the target range (70-180mg/dL). Hypoglycemia events are effectively prevented, and severe hyperglycemiaevents are reduced. The RL approach also leads to a statistically significantreduction in average daily basal insulin dosage compared to conventionaltherapy. These findings highlight the effectiveness of the multi-agent RLapproach in achieving better glucose control and mitigating the risk of severehyperglycemia in individuals with T1D.

Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology