Adversarial cooperative imitation learning for dynamic treatment

A method for responding to changing conditions, comprising:

• training a model on historical treatment trajectories, using a processor, including trajectories that resulted in a positive health outcome and trajectories that resulted in a negative health outcome;
• generating a dynamic treatment regime for a patient using the trained model and patient information;
• and
• treating the patient in accordance with the dynamic treatment regime, in a manner that is responsive to changing patient conditions, by triggering one or more medical devices to administer a treatment to the patient.

A system for responding to changing conditions, comprising:

• a machine learning model, configured to generate a dynamic response regime for using environment information;
• a model trainer, configured to train the machine learning model, including trajectories that resulted in a positive outcome and trajectories that resulted in a negative outcome, by using an adversarial discriminator to train the machine learning model to generate trajectories that are similar to historical trajectories that resulted in a positive outcome, and by using a cooperative discriminator to train the model to generate trajectories that are dissimilar to historical trajectories that resulted in a negative outcome;
• and
• a response interface, configured to trigger a response to changing environment conditions in accordance with the dynamic response regime.