End-to-end Offline Reinforcement Learning for Glycemia Control
Tristan Beolet Alice Adenis Erik Huneker Maxime Louis
The development of closed-loop systems for glycemia control in type I
diabetes relies heavily on simulated patients. Improving the performances and
adaptability of these close-loops raises the risk of over-fitting the
simulator. This may have dire consequences, especially in unusual cases which
were not faithfully-if at all-captured by the simulator. To address this, we
propose to use offline RL agents, trained on real patient data, to perform the
glycemia control. To further improve the performances, we propose an end-to-end
personalization pipeline, which leverages offline-policy evaluation methods to
remove altogether the need of a simulator, while still enabling an estimation
of clinically relevant metrics for diabetes.