  Artificial intelligence, particularly through recent advancements in deep
learning, has achieved exceptional performances in many tasks in fields such as
natural language processing and computer vision. In addition to desirable
evaluation metrics, a high level of interpretability is often required for
these models to be reliably utilized. Therefore, explanations that offer
insight into the process by which a model maps its inputs onto its outputs are
much sought-after. Unfortunately, the current black box nature of machine
learning models is still an unresolved issue and this very nature prevents
researchers from learning and providing explicative descriptions for a model's
behavior and final predictions. In this work, we propose a novel framework
utilizing Adversarial Inverse Reinforcement Learning that can provide global
explanations for decisions made by a Reinforcement Learning model and capture
intuitive tendencies that the model follows by summarizing the model's
decision-making process.
