  There is a growing desire in the field of reinforcement learning (and machine
learning in general) to move from black-box models toward more "interpretable
AI." We improve interpretability of reinforcement learning by increasing the
utility of decision tree policies learned via reinforcement learning. These
policies consist of a decision tree over the state space, which requires fewer
parameters to express than traditional policy representations. Existing methods
for creating decision tree policies via reinforcement learning focus on
accurately representing an action-value function during training, but this
leads to much larger trees than would otherwise be required. To address this
shortcoming, we propose a novel algorithm which only increases tree size when
the estimated discounted future reward of the overall policy would increase by
a sufficient amount. Through evaluation in a simulated environment, we show
that its performance is comparable or superior to traditional tree-based
approaches and that it yields a more succinct policy. Additionally, we discuss
tuning parameters to control the tradeoff between optimizing for smaller tree
size or for overall reward.
