To better capture and adapt to the evolving dynamics of a patient's respiratory state during prolonged ventilation events, the RL agents can be designed with adaptive learning capabilities and continuous monitoring of patient responses. One approach is to incorporate online learning mechanisms that allow the RL agent to update its policies in real-time based on feedback from the patient's physiological responses. By leveraging reinforcement learning with online updates, the agent can dynamically adjust ventilation settings to accommodate changes in the patient's condition and optimize outcomes over time. Additionally, integrating predictive modeling techniques, such as recurrent neural networks or time-series analysis, can help the RL agent anticipate future trends in the patient's respiratory state and proactively adjust ventilation strategies. By combining adaptive learning with predictive modeling, the RL agents can better capture the complex and dynamic nature of lung physiology, leading to more personalized and effective ventilation control strategies.