  Reinforcement learning techniques successfully generate convincing agent
behaviors, but it is still difficult to tailor the behavior to align with a
user's specific preferences. What is missing is a communication method for the
system to explain the behavior and for the user to repair it. In this paper, we
present a novel interaction method that uses interactive explanations using
templates of natural language as a communication method. The main advantage of
this interaction method is that it enables a two-way communication channel
between users and the agent; the bot can explain its thinking procedure to the
users, and the users can communicate their behavior preferences to the bot
using the same interactive explanations. In this manner, the thinking procedure
of the bot is transparent, and users can provide corrections to the bot that
include a suggested action to take, a goal to achieve, and the reasons behind
these decisions. We tested our proposed method in a clone of the video game
named \textit{Super Mario Bros.}, and the results demonstrate that our
interactive explanation approach is effective at diagnosing and repairing bot
behaviors.
