## 4 Experimental Evaluations and Results
### Analysis of Shapley-Based Explainer  
If we sum the attribution values of input features towards the RSRP reward component on Figure 8 (bottom-right), we will obtain a negative value of the RSRP reward component. However, the predicted Q-value of this component is positive, as shown as a black bar on Figure 9b. When the explainer is built on the Shapley-concept (e.g., SHAP and its variants), the sum of attribution (i.e., feature importance) values equals to the output (or prediction) of the performing model. This value includes the baseline value, which is the average model output of the given dataset, where we present it as the purple bar in Figure 9b. In this manner, we have a complete explanation in which the predicted Q-value (black bar) is equal to the sum of all input features contributions including the base value of the reward component.  
In an implementation without decomposition, the baseline values are different for possible actions (or classes in a supervised learning problem). Thus, we can consider this baseline value as the bias towards a different action. In a model with reward decompositions, the difference in the base value appears not only for different actions but also for reward components, as shown by Table 1. Although these values are constant for any instance/state/observation, they play an important role in selecting the action. The differ  
Figure 8: The local explanations of one antenna when the agent chose no-tilt action.  
Figure 9: Complete explanation where the sum of feature attribution and base values equals the predicted Q-values. (**a**) Attribution and Q-values at the Q-level. (**b**) Attribution and Q-values on RL level.  
ences in the base value among different actions are not as large as the difference between different reward components, as shown by the range on Table 1 (the range is the subtraction of the maximum by the minimum value of each reward component). However, the mean values show the average base value of each reward component, where the SINR has the lowest average base value and the throughput component has the highest value.  
When analyzing a local explanation, it is important that the explanation be accompanied by the base value as a reference to show all contributions leading to the predicted Q-value. In this manner, the explanation shows how each input feature impacts the model's output from the average situation. Although understanding why a specific base value is obtained remains limited to the fact that it is the average output value of the model from the given dataset, exposing it informs the complete analysis of the model. The dataset becomes the scope or corridor for generating the explanation. In connection with the normalized explanations, in Figure 7b, the information shows the importance of different model elements in terms of the variation of input values regardless of the bias of the reward component. In connection with the RL level explanation Figure 6b, the bias of the reward component may be overcome by the contribution made by the input feature(s). Specifically, for the experiment above, the mean bias (base value) for the throughput reward component (18.164) is higher than the RSRP (12.548). However, the mean absolute contribution of the RSRP input feature to the RSRP reward component is 15.501 where the sum of it and its bias (28.049) can overcome the throughput bias. This explanation shows that the model has bias towards different components, and the variation of the input values may overcome it.