## 4 Results
### Comparison of the Image-like Representations  
The image-like representations are compared in the same manner as mentioned above. Hence on the same one thousand episodes with the same random seeds, consequently on the same test episodes. However, the evaluation is divided into three parts. In the first part, the versions trained for only the original network are evaluated. The performance in terms of the number of steps used for solving the test episodes is shown in Figure 8. The proposed representation and the Flatland global observation reach a success rate higher than 90%. Still, our approach, trained with the same parameters, solves 6% more test episodes without deadlock than the Flatland global observation. Furthermore, by checking the left and middle areas of Figure 8, it can be seen that our approach utilizes fever steps on average, hence providing a better solution for the control problem overall.  
The second part compares the versions trained for the mirrored network layout. The characteristics of the results are nearly the same as the previous one. The comparison is shown in Figure 9. Both solutions provide success rates above 90%, but our approach outperforms the Flatland global observation with approximately 2%, thus utilizing fever steps for solving the test episodes. In the case of the image-like representation, the performance difference is not that significant when the agents only have to learn one network during the entire training process. However, the source definitely, through the additional channels in the proposed state representation that enable a better understanding of the control problem for the agents, has an improved performance.  
In the third part, the algorithms trained simultaneously for both network layouts are compared. The evaluation procedure is the same as in the previous cases. The per  
Figure 8: Comparison of the different solutions for the original network.  
Figure 9: Comparison of the different solutions for the mirrored network.  
formance discrepancy should be significant based on the convergence plot introduced above. The result is shown in Figure 10. Comparing the characteristics of the proposed method that trained for both network layouts, it can be seen that it nearly reaches the same performance as in the case when exclusively trained for only one network type, which clearly demonstrates the potential for generalization. In the meantime, the Flatland global observation suffers a remarkable performance discrepancy compared to the cases when only trained for a sole network type. Compared to each other, the proposed representation maintains the success rate above 90%, while the other solutions struggle at approximately 60%. Moreover, when it solves the given test episodes, it utilizes a greater number of steps, thus providing an inferior performance compared to the proposed representation's results. When the agents are trained for both the mirrored and the original network, the performance improvement of the proposed representation arises from the additional channel that clearly defines the network architecture. With this channel, the agent can understand the concept of the free path, and thanks to that, it can generalize through different layouts.