## 6 Discussion  
In our study, we conducted a quantitative analysis of learning performance, comparing agents using the eDA3-X model to those utilizing the baseline method. We found that agents equipped with eDA3-X not only exhibited improved interpretability but also outperformed agents using the baseline method. As previously reported in the literature, the attention mechanism provides agents with the ability to adapt flexibly to their environment [18]. By leveraging the attention weights to selectively focus on important segments of their observations, agents utilizing eDA3-X achieved more efficient learning performance compared to those relying on the baseline methods. Notably, the improvement in learning performance was particularly remarkable when employing a simple neural network architecture (specifically, DQN in this paper) within the DRL head in eDA3-X. This finding suggests that the limited information available in observations can be leveraged more effectively for learning performance when using a simple reinforcement learning algorithm. Through experiments and attention analysis, we validated the interpretability of eDA3-X agents by examining their standard, class, and positional attention heatmaps. Our analysis of the standard attention heatmaps and cooperative behaviors indicates that eDA3-IQN agents selectively assign attention weight to their observations based on the intensity of interest, focusing on nearby objects when multiple objects are present. Additionally, these agents give high attention to other agents. When encountering agents that behave randomly, the eDA3-X agents seem to learn that adaptation is not worthwhile. While this feature has been previously reported, in this paper, we further classified standard attention into class and positional attention for a more granular analysis. The comparison of attention weight in the class attention may allow one to interpret the efficiency of eDA3-X agents to perceive each element type in the observation regardless of location. For example, as illustrated in Figure 4b,c, our analysis points towards some agent considering another similar agent more important than objects, while it deems a randomly behaving agent less significant. Furthermore, we discussed the potential of positional attention conveys distinct meanings based on either a local or relative observation method. In the case of the local view method, the positional attention may reveal the areas where eDA3-X agents pay attention, as demonstrated in Figure 4. Conversely, with the relative view method, the positional attention may indicate the region an agent learned to be responsible for. In addition to the above analysis, we examined the development of positional attention throughout training. As shown in Figures 7 and 8, the agents assign positional attention weight based on relevant information, such as frequently observed agents, objects, and edge of the environment. As previously mentioned, eDA3-X agents using the local view method focus on their four adjacent cells and the area where other agents are often observed, while those using the relative view method focus on their assigned region. We believe that this difference in positional attention contributes significantly to the disparity in learning performance. Limitations: For our approach, there may still be limitations in terms of quantitatively evaluating the interpretability of agents. In this study, we introduced the observation mechanism of eDA3-X agents through attention heatmaps, for which we illustrated the potential value for explanations. However, the lack of quantitative evaluation on explainability may be critical in safety-critical applications such as self-driving systems, where high safety and algorithmic accountability must be ensured. Hence, to overcome such limitations, our next study will focus on conducting a quantitative examination of the decision-making process transparency of the agents through attention heatmaps.