  Due to the capability of deep learning to perform well in high dimensional
problems, deep reinforcement learning agents perform well in challenging tasks
such as Atari 2600 games. However, clearly explaining why a certain action is
taken by the agent can be as important as the decision itself. Deep
reinforcement learning models, as other deep learning models, tend to be opaque
in their decision-making process. In this work, we propose to make deep
reinforcement learning more transparent by visualizing the evidence on which
the agent bases its decision. In this work, we emphasize the importance of
producing a justification for an observed action, which could be applied to a
black-box decision agent.
