  High-dimensional policies, such as those represented by neural networks,
cannot be reasonably interpreted by humans. This lack of interpretability
reduces the trust users have in policy behavior, limiting their use to
low-impact tasks such as video games. Unfortunately, many methods rely on
neural network representations for effective learning. In this work, we propose
a method to build predictable policy trees as surrogates for policies such as
neural networks. The policy trees are easily human interpretable and provide
quantitative predictions of future behavior. We demonstrate the performance of
this approach on several simulated tasks.
