Romp

ROMP (Regularized Off-Policy Optimization with Multiple policies) is a machine learning method used to improve decision-making algorithms, especially in areas like healthcare or finance. It focuses on learning the best actions by analyzing data collected from different policies (rules or strategies) without trying to replicate the exact policies that generated the data. ROMP combines regularization techniques to prevent overfitting and ensures stable, reliable improvements. Essentially, it helps develop smarter, more effective strategies from existing data, even when that data was gathered under various policies, leading to better decision-making in complex, real-world situations.