  Reliable real-world deployment of reinforcement learning (RL) methods
requires a nuanced understanding of their strengths and weaknesses and how they
compare to those of humans. Human-machine systems are becoming more prevalent
and the design of these systems relies on a task-oriented understanding of both
human learning (HL) and RL. Thus, an important line of research is
characterizing how the structure of a learning task affects learning
performance. While increasingly complex benchmark environments have led to
improved RL capabilities, such environments are difficult to use for the
dedicated study of task structure. To address this challenge we present a
learning environment built to support rigorous study of the impact of task
structure on HL and RL. We demonstrate the environment's utility for such study
through example experiments in task structure that show performance differences
between humans and RL algorithms.
