## 4 System Implementation and Experimental Results
(2-dimension) at time \(t\), (**b**) Possible Actions and the Relevant Statutes (2-dimension), and (**c**) Distribution for Possible Actions and the Following Status. In this study, a soccer game was supposed, and an experiment was carried out through the example of learning to deliver the ball to allies while avoiding the enemy network that obstructed the flow of the ball. The players of both teams do not stay in a fixed position but have a distribution over a defined range and move dynamically; thereby, changing the network topology. The human evaluator visually confirms the degree of obstruction of the path for the ball to be delivered by the enemy and enters a real number between 0 to 1 accordingly. When a game agent passes a ball without any intervene, an expert (human) evaluates the pass closed to 1 using the developed system. The human's evaluations are subjective and influenced by the locations of the opponents and other reasons. The evaluation score modifies the reward values using Equations (9) and (10). Then, the game agent changes the passing action to obtain higher reward using the provided algorithm shown in Table 2. In the iterative learning, the human evaluation reward is calculated and updated by the provided calculations of the reward value of the system itself and the human evaluation reward value by the cooperative human-machine evaluation framework. Figure 6 shows the results of learning a soccer game represented by two networks that coexist using the proposed cooperative human-machine evaluation framework. There are two algorithms prepared for comparisons with the proposed cooperative human-machine evaluation framework. Figure 5: Implemented Soccer Game using the Proposed Cooperative Human–Machine Evaluation. Framework Considering Competitive Network with Dynamic Topology Changes. The graph of Figure 5(a) represented by the symbol \"o\" signifies the reward of cooperative human-machine evaluation framework in each iteration as calculated by the algorithm of Table 2 and the graph of Figure 5(a) represented by the symbol \"*\" signify the value of the average value of the reward of a cooperative human-machine evaluation framework. Figure 5(b) is an algorithm in which the human evaluation is evaluated in binary (of 0 and 1). Moreover, the graphs of Figure 5(b) represented by the symbol \"o\" signify the reward of the simple evaluation in binary in each iteration, and the graph of Figure 5(b) represented by the symbol \"*\" signifies the average reward of evaluated in binary strategy. Figure 5(c) is an algorithm in which the traditional MDP method without human intervention. The graphs of Figure 5(c) represented by the symbol \"o\" means the reward of the MDP method in each iteration and the graph of Figure 5(c) represented by the symbol \"*\" signify the average reward of evaluated in the MDP method. As seen in Figure 6, the proposed cooperative human-machine evaluation achieved the fastest convergence. This result is considered to be the result of human intervention that made quick decisions with specialized knowledge, unlike the way of updating only the reward of the system composed of existing learning objects. This study proposed a strategic method that aims to adaptively and quickly update the human evaluation scale and converge quickly to the maximum value.