  The widespread adoption of effective hybrid closed loop systems would
represent an important milestone of care for people living with type 1 diabetes
(T1D). These devices typically utilise simple control algorithms to select the
optimal insulin dose for maintaining blood glucose levels within a healthy
range. Online reinforcement learning (RL) has been utilised as a method for
further enhancing glucose control in these devices. Previous approaches have
been shown to reduce patient risk and improve time spent in the target range
when compared to classical control algorithms, but are prone to instability in
the learning process, often resulting in the selection of unsafe actions. This
work presents an evaluation of offline RL for developing effective dosing
policies without the need for potentially dangerous patient interaction during
training. This paper examines the utility of BCQ, CQL and TD3-BC in managing
the blood glucose of the 30 virtual patients available within the FDA-approved
UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the
total training samples required by online RL to achieve stable performance,
this work shows that offline RL can significantly increase time in the healthy
blood glucose range from 61.6 +\- 0.3% to 65.3 +/- 0.5% when compared to the
strongest state-of-art baseline (p < 0.001). This is achieved without any
associated increase in low blood glucose events. Offline RL is also shown to be
able to correct for common and challenging control scenarios such as incorrect
bolus dosing, irregular meal timings and compression errors.
