  Interactive reinforcement learning, where humans actively assist during an
agent's learning process, has the promise to alleviate the sample complexity
challenges of practical algorithms. However, the inner workings and state of
the robot are typically hidden from the teacher when humans provide feedback.
To create a common ground between the human and the learning robot, in this
paper, we propose an Augmented Reality (AR) system that reveals the hidden
state of the learning to the human users. This paper describes our system's
design and implementation and concludes with a discussion on two directions for
future work which we are pursuing: 1) use of our system in AI education
activities at the K-12 level; and 2) development of a framework for an AR-based
human-in-the-loop reinforcement learning, where the human teacher can see
sensory and cognitive representations of the robot overlaid in the real world.
