The proposed interpretable RL methodology can be extended to incorporate additional clinical constraints and objectives by integrating a multi-objective optimization approach. This would involve defining a more comprehensive reward function that considers not only SpO2 and aggressive ventilator settings but also other vital parameters such as PaO2, PaCO2, and respiratory rate. By assigning weights to each objective based on clinical priorities, the RL agent can learn to balance the trade-offs between different goals while optimizing ventilation strategies. Moreover, incorporating safety constraints, such as limiting the duration of high-risk ventilator settings or avoiding rapid changes in settings, can further enhance the clinical relevance of the learned policies. By expanding the reward function and constraints, the RL agent can be guided to make decisions that align more closely with clinical objectives and patient outcomes.