## 4 System Implementation and Experimental Results
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Panel Name** & **Detailed Function** & \multicolumn{1}{c}{**Configurations**} \\ \hline Application for basketball AI game & Define an iteration and defense, attack range & - & Iteration Defense range Attack range Attack range \\ \hline Basketball AI game player network and system reward calculation & Check the network of attacking players and calculate and show system rewards & - & Network topology of attacking players System reward monitor \\ \hline Adaptive human evaluation estimation and calculation & Distribution of position changes according to the repetition learning time point. & - & Player position distribution (2D) Player position distribution (3D) Player position distribution (3D) \\ \hline Adaptive human evaluation estimation and calculation & Enter the reward by evaluating the condition through human intervention in repetitive learning. The reward entered is estimated and mapped to a normal distribution. & - & Human evaluation reward estimate chart Adaptive human evaluation calculation monitor \\ \hline Updating human evaluation reward using SGD algorithm & Update human evaluation reward adaptively using SGD algorithm & - & Chart of updating human evaluation reward using SGD algorithm \\ \hline Reward update monitoring & Derivation of the reward value obtained as a result of learning for each iteration. & - & Chart of analyzing integrated reward Iteration, average reward Reward at time \(t\) Reward at time \(t\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Function summary of adaptive human evaluation strategy framework implementation using the SGD algorithm for RL of dynamic complex network. Figure 7 compares the RL methods using the adaptive human evaluation strategy proposed in this paper with those proposed in previous studies. The existing methods that were compared with the  
Figure 5: Player position network topology (2D and 3D) in basketball game problem. Figure 6: Process of calculating the final reward strategically using the SDG algorithm. proposed method were human evaluation with binary updating, the SARSA method, and the MDP method. Table 3 defines and shows experimental conditions that apply equally to all methods. All of the methods involve learning basketball games with the complex and dynamically changing network topologies discussed in Section 3. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Experimental Conditions**} \\ \hline Network condition & Number of networks: 2 \\ Number of nodes: 5 in each network \\ \hline Iteration & 100 \\ \hline Discount rate & 0.9 \\ \hline Learning rate & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Definition of experimental conditions for complex and dynamically changing network topologies. Figure 7: Comparison of four RL methods for complex and dynamically changing network topologies. As shown in Table 4, the type and human intervention rate of human evaluation of each method, the average reward value, the Q-function value was calculated, and the points of convergence with the maximum reward value were compared. As shown in Table 4, the RL method with the proposed adaptive human evaluation strategy achieved convergence with the highest maximum reward value. In addition, it exhibits the fastest convergence to the maximum reward in Figure 7. This result demonstrates that, unlike when the method of updating the system reward using the existing learning objects is used, the learning reaches the maximum reward value faster when the quantitative evaluation is performed through human intervention.