Sequential decision-making is crucial when managing HIV treatments, as drug effectiveness can wane over time. A model-based reinforcement learning approach is employed to simulate potential outcomes based on patient data, incorporating uncertainty to estimate the effects of different drug cocktails. This innovative method allows for effective learning and evaluation without direct access to real patient data.
