  This study developed an explainable AI for ship collision avoidance.
Initially, a critic network composed of sub-task critic networks was proposed
to individually evaluate each sub-task in collision avoidance to clarify the AI
decision-making processes involved. Additionally, an attempt was made to
discern behavioral intentions through a Q-value analysis and an Attention
mechanism. The former focused on interpreting intentions by examining the
increment of the Q-value resulting from AI actions, while the latter
incorporated the significance of other ships in the decision-making process for
collision avoidance into the learning objective. AI's behavioral intentions in
collision avoidance were visualized by combining the perceived collision danger
with the degree of attention to other ships. The proposed method was evaluated
through a numerical experiment. The developed AI was confirmed to be able to
safely avoid collisions under various congestion levels, and AI's
decision-making process was rendered comprehensible to humans. The proposed
method not only facilitates the understanding of DRL-based controllers/systems
in the ship collision avoidance task but also extends to any task comprising
sub-tasks.
