Diabetes affects millions globally, especially in type 1 cases where precise blood glucose control is crucial. Current methods, relying on patient monitoring and insulin administration, often fall short. Hence, there’s interest in using reinforcement learning (RL) to optimize management. RL, a type of machine learning, shows promise in adjusting insulin dosages based on feedback. Our study examines a Proximal Policy Optimization (PPO) agent trained on average patient data, showing its effectiveness across diverse patient profiles. Our findings highlight the adaptability of PPO-based controllers in managing blood glucose levels effectively.