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