Interpretable Reinforcement Learning with Ensemble Methods

Reinforcement learning continues to break bounds on what we even thought possible, recently with AlphaGo's triumph over leading Go player Lee Sedol and with the further successes of AlphaGoZero, which surpassed AlphaGo learning only from self-play [14]. While the performance of such systems is impressive and very useful, sometimes it is desirable to understand and interpret the actions of a reinforcement learning system, and machine learning systems in general. These circumstances are more common in high-pressure applications, such as healthcare, targeted advertising, or finance [6]. For example, researchers at the University of Pittsburgh Medical Center trained a variety of machine learning models including neural networks and decision trees to predict whether pneumonia patients might develop severe complications. The neural networks performed the best on their testing data, but upon examination of the rules of the decision trees, the researchers found that the trees recommended sending pneumonia patients who had asthma directly home, despite the fact that asthma makes patients with pneumonia much more likely to suffer complications.