Method, System, and Computer Program Product for Use of Reinforcement Learning to Increase Machine Learning Model Label Accuracy

Provided are methods that include receiving an initial training dataset, where each data instance of the initial training dataset has a label and a first percentage of the plurality of data instances are labeled correctly, providing the initial training dataset as an input to a reinforcement learning agent (RLA) machine learning model to generate a second training dataset, where a second percentage of data instances of the second training dataset are labeled correctly and the second percentage is greater than the first percentage, training a deep learning model using the second training dataset to provide a trained deep learning model, testing the trained deep learning model using a testing dataset to generate a resultant dataset that has a detection rate, which is an indication of a number of data instances that were predicted correctly, and generating a reward parameter. Systems and computer program products are also disclosed.