  Visualization tools for supervised learning have allowed users to interpret,
introspect, and gain intuition for the successes and failures of their models.
While reinforcement learning practitioners ask many of the same questions,
existing tools are not applicable to the RL setting. In this work, we describe
our initial attempt at constructing a prototype of these ideas, through
identifying possible features that such a system should encapsulate. Our design
is motivated by envisioning the system to be a platform on which to experiment
with interpretable reinforcement learning.
