Implement of Behavior Cloning (BC) and Conservative Q-Learning (CQL) algorithms for training reinforcement learning models using a dataset of state-action pairs. It provides an environment for experimenting with these algorithms, comparing their performance, and analyzing the effects of different parameters and dataset variations on training result

We present GUIDE (Glucose Intelligence Decision Engine), an RL-based framework that generates patient-specific recommendations for insulin and carbohydrate intake, adapting to individual glucose dynamics and daily routines