AUTOMATED EXPLAINER OF REINFORCEMENT LEARNING ACTIONS USING OCCUPATION MEASURES

摘要

Automatic identification of features that drive a reinforcement learning model to recommend an action of interest. The identification is based on a calculation of occupation measures of state-action pairs associated with the reinforcement learning model. High occupation measures of certain action-state pairs indicate that the states of these pairs likely include the sought-after features.