  Global explanations of a reinforcement learning (RL) agent's expected
behavior can make it safer to deploy. However, such explanations are often
difficult to understand because of the complicated nature of many RL policies.
Effective human explanations are often contrastive, referencing a known
contrast (policy) to reduce redundancy. At the same time, these explanations
also require the additional effort of referencing that contrast when evaluating
an explanation. We conduct a user study to understand whether and when
contrastive explanations might be preferable to complete explanations that do
not require referencing a contrast. We find that complete explanations are
generally more effective when they are the same size or smaller than a
contrastive explanation of the same policy, and no worse when they are larger.
This suggests that contrastive explanations are not sufficient to solve the
problem of effectively explaining reinforcement learning policies, and require
additional careful study for use in this context.
