## 4 Experimental Evaluations and Results
### Usages with BEERL  
By presenting the explanations as above, we can gain more insight about the trained agent than the available methods by noting some findings, such as the following:  
* The RSRP input feature is the most contributing feature and significantly affects the RSRP reward component. This explanation shows in detail the contribution of each input feature to every output component and how they are correlated, as shown in Figures 5a and 6a. * SINR and throughput reward components are correctly focused on SINR and throughput input features, respectively. It shows that the Q-function performs well in focusing on the respective feature. * The SINR and throughput reward components have less contribution than RSRP reward component even on the Q-function level. Applying the reward weights pushes the contribution further, where RSRP has a significantly larger contribution than other components. * The base values show the bias of different actions and reward components. Even though the RSRP reward component is weighted twice that of others, the throughput reward component remains as the highest. This means that a higher contribution from the input feature(s) is required if other component(s) need to be overruled in selecting the action. * If there is a situation where the priority of the reward components needs to be adjusted, one can reuse the trained model and then only adjust the reward weight and continue the training without retraining it from scratch with a random policy. With the given explanations, we are confident that the model is performing well because each reward component focuses on the correct features. This information presents the correctness of the proposed method, which not only increases human trust in the AI used but also allows for the transfer of knowledge with stronger reason. From the above points, the explanations improve human knowledge to understand the behavior of the model in different elements of the RL model. The effect of the reward weight, the bias of actions and the reward component, and the contribution of the input feature and the reward components are produced to inform the detailed properties of the model's behavior. An indication of a better understanding is that the agent can be adjusted or reconfigured to fit the necessary goal. In the following, we show the adjustment  
Figure 10: Contrastive explanations generated using BEERL where the MSX is highlighted. (a) Contrastive explanations about why no tilt action is chosen rather than tilting down. (b) Contrastive explanations about why no tilt action is chosen rather than tilting up. of the reward weights to reduce the bias (difference of the base value) among different reward components. As the mean base values at the Q-function level are 6.274168, 8.767802, and 18.163904 for the RSRP, SINR, and throughput reward component, respectively, we adjusted the reward weight to achieve similar values. We choose an arbitrary value (in this case, it is 10) that we divide by the mean values of the base values of the reward components. Therefore, to keep two decimal values, we set the new reward weight at 1.60, 1.14, and 0.55 for RSRP, SINR, and throughput, respectively. Subsequently, we conducted an evaluation process and obtained the explanations, including bias, as shown in Figure 11 and Table 2.  
As the reward adjustment is made relative to the trained agent, the explanations on the Q-function level are similar to Figure 5. From Figure 11, we can see that the throughput input feature has a relatively similar contribution to the SINR and throughput reward components. The importance of the characteristics in each reward component is also changed following the updated reward weights, as shown in Figure 11. Table 2 shows the base values after the reward weights were adjusted, resulting in a reduction in the bias of the reward components. This weight adjustment exemplifies the possibility of reconfiguring the RL agent after training to achieve the desired properties.