## 3 Results
### Illustration and Evaluation of Algorithm Performance
In addition, after increasing the number of resources, the network is no longer congested; thus, all the delay curves, including that of QL with user cooperation considered, will decline and their slopes will gradually decrease. In Figure 8c, because the transmission energy consumed by a network resource is significantly less than the energy consumption of device access and standby in the model described in this paper, the curve in this figure is flatter than that in Figure 7c. Figure 9 shows the effect of changing the power gears on optimization. The content of each axis is similar to that in Figure 7. With an increase in gears, there are more optional actions for allocation. Likewise, the performance of the QL algorithm was better than others. In Figure 9, the performances of the algorithms also showed considerable differences after changing the G value. QL and PSO gained some benefits from the increase in gears, but PSO was not as stable as QL. The DE algorithm was not very sensitive to the increase in G. Other algorithms could not provide stable optimization results and gradually converged on sub-optimal solutions. To summarize, the gear of power allocation depends on the performance of the individual algorithm. Blindly increasing the gears of power allocation may exceed the ability of the optimization algorithm and result in unwanted side-effects. Increasing the granularity of power allocation will significantly increase the exploration space of the optimization algorithm. In the wireless network scenario with dramatic  
Figure 8: Optimization performance curve of variable N (M = 3, G = 1): (**a**) curve for value of objective function; (**b**) curve for time consumption; (**c**) curve for energy consumption. fluctuations, the QL algorithm can increase its experience in solving problems by recording the rewards obtained, so as to find the solutions to optimization problems with a small amount of data. This is a property that other algorithms do not have. It can be seen from Figure 9a that the QL algorithm steadily gained benefits from more power allocation actions. After considering user cooperation, the algorithm can save network resources, compress exploration space and obtain better network performance. This is in line with the idea of equivalence and the compression of state space proposed in Section Introduction of Qlearning Components. It can be further seen from Figure 9b,c that the compression of the exploration space will reduce the burden of optimization and improve the optimization results with respect to delay and energy consumption. Figure 10 shows the impact of a different number of similar tasks on optimization. The sequence and axes of each image are similar to that in Figure 7. The abscissa refers to the number of similar tasks after considering user cooperation. In this experiment, taking a scenario of four tasks as an example, we changed the number of similar downlink tasks and observed the optimization performance of the QL algorithm. It is obvious that with the increase in task similarity, more tasks in the MEC network can be integrated and processed together. Thus, the network performance can be further improved. Considering user cooperation can significantly reduce the value of the objective function, further improve the processing speed of mobile applications and increase network optimization without introducing more energy consumption. In addition, the dotted line in the left figure illustrates the difference (magnified by a factor 5) in the optimization results before and after user cooperation. In this simulation, the tasks included similar downlink tasks and other kinds of tasks. In the model, the delay and energy consumption corresponding to downlink tasks are smaller than other kinds of tasks. Thus, as similar tasks increase and other types of tasks decrease, the objective function curves of the two QL algorithms, as shown in Figure 10a, also gradually decrease.