  Addressing the need for explainable Machine Learning has emerged as one of
the most important research directions in modern Artificial Intelligence (AI).
While the current dominant paradigm in the field is based on black-box models,
typically in the form of (deep) neural networks, these models lack direct
interpretability for human users, i.e., their outcomes (and, even more so,
their inner working) are opaque and hard to understand. This is hindering the
adoption of AI in safety-critical applications, where high interests are at
stake. In these applications, explainable by design models, such as decision
trees, may be more suitable, as they provide interpretability. Recent works
have proposed the hybridization of decision trees and Reinforcement Learning,
to combine the advantages of the two approaches. So far, however, these works
have focused on the optimization of those hybrid models. Here, we apply
MAP-Elites for diversifying hybrid models over a feature space that captures
both the model complexity and its behavioral variability. We apply our method
on two well-known control problems from the OpenAI Gym library, on which we
discuss the "illumination" patterns projected by MAP-Elites, comparing its
results against existing similar approaches.
