  The learning process of a reinforcement learning (RL) agent remains poorly
understood beyond the mathematical formulation of its learning algorithm. To
address this gap, we introduce attention-oriented metrics (ATOMs) to
investigate the development of an RL agent's attention during training. We
tested ATOMs on three variations of a Pong game, each designed to teach the
agent distinct behaviours, complemented by a behavioural assessment. Our
findings reveal that ATOMs successfully delineate the attention patterns of an
agent trained on each game variation, and that these differences in attention
patterns translate into differences in the agent's behaviour. Through
continuous monitoring of ATOMs during training, we observed that the agent's
attention developed in phases, and that these phases were consistent across
games. Finally, we noted that the agent's attention to its paddle emerged
relatively late in the training and coincided with a marked increase in its
performance score. Overall, we believe that ATOMs could significantly enhance
our understanding of RL agents' learning processes, which is essential for
improving their reliability and efficiency.
