Contrastive Explanations for Reinforcement Learning via Embedded Self
  Predictions
  We investigate a deep reinforcement learning (RL) architecture that supports
explaining why a learned agent prefers one action over another. The key idea is
to learn action-values that are directly represented via human-understandable
properties of expected futures. This is realized via the embedded
self-prediction (ESP)model, which learns said properties in terms of human
provided features. Action preferences can then be explained by contrasting the
future properties predicted for each action. To address cases where there are a
large number of features, we develop a novel method for computing minimal
sufficient explanations from anESP. Our case studies in three domains,
including a complex strategy game, show that ESP models can be effectively
learned and support insightful explanations.
