  Explainable AI techniques that describe agent reward functions can enhance
human-robot collaboration in a variety of settings. One context where human
understanding of agent reward functions is particularly beneficial is in the
value alignment setting. In the value alignment context, an agent aims to infer
a human's reward function through interaction so that it can assist the human
with their tasks. If the human can understand where gaps exist in the agent's
reward understanding, they will be able to teach more efficiently and
effectively, leading to quicker human-agent team performance improvements. In
order to support human collaborators in the value alignment setting and similar
contexts, it is first important to understand the effectiveness of different
reward explanation techniques in a variety of domains. In this paper, we
introduce a categorization of information modalities for reward explanation
techniques, suggest a suite of assessment techniques for human reward
understanding, and introduce four axes of domain complexity. We then propose an
experiment to study the relative efficacy of a broad set of reward explanation
techniques covering multiple modalities of information in a set of domains of
varying complexity.
