## 5 Results
### Results
Overall, the DQL-edge method consistently yields better results compared to the DQL-local, reinforce, and greedy methods in all other tested configurations. Next, we compare the mobile and edge processor utilization of our proposed method, DQL-edge, with DQL-local, Reinforce-edge, greedy, and locally executing all tasks. As can be seen from Figure 4, to minimize the power consumption of mobile devices, the algorithms offload most tasks to edge devices when there are abundant processing resources in edge devices, but when the resources on edge devices are constrained, they decide to execute more tasks locally. Even though all methods struggle to fully utilize edge processors in the centered with 100 mobile nodes configuration, DQL-edge manages to achieve an almost 90 percent edge processor utilization, which is higher than all other methods in that configuration. Lastly, the cost values of an exhaustive experiment are presented in Table 2. These compare our proposed method, DQL-edge, with all other six methods, which are DQL-local, reinforce-edge, greedy, randomly choosing whether to offload a task (random), and all tasks executed locally (local) or remotely (remote). It also examines seven configurations of task arrival rates, \(\lambda\), and mobile node counts in both centered and uniform settings. \(\mu\) and \(\sigma\) are the average cost and standard deviation of tasks for the entire duration of the simulation, and the graph underneath is the average cost as a function of time in the simulation. The graph, therefore, depicts the trend for cost as time passes. If the trend is upwards and the  
Figure 2: Comparing the cost, delay, and power consumption of the DQL-edge, DQL-local, reinforce-edge, greedy, random, and local task-offloading models for the duration of the simulation. The simulation was run for both centered and uniform positioning of mobile devices and when the number of mobile devices was 50 and 100.  
standard deviation is high, it means that the model for the given configuration is unstable and unable to achieve a bounded cost if the simulation were to continue. Otherwise, the method is considered stable for the given configuration. As can be seen from the table, DQL-edge yields better results in almost all configurations when compared to other methods. When the mobile node count is low or the task arrival rate is low, this difference is minor, but when we increase either one, DQL-edge performs significantly better than any other method, especially in the centered configuration. Figure 4: Comparing the mobile and edge processor utilization of the DQL-edge, DQL-local, reinforce-edge, greedy, and local task offloading models for the duration of the simulation with an extra 50 s after the task arrival process is stopped. The simulation was run for both centered and uniform positioning of mobile devices and when the number of mobile devices was 25, 50, 75, and 100. Figure 3: Comparing the cost, delay, and power consumption of the DQL-edge, DQL-local, reinforce-edge, and greedy task offloading models for the duration of the simulation. The simulation was run for both centered and uniform positioning of mobile devices and when the number of mobile devices was 25, 50, 75, and 100.  
that take advantage of trajectories of states and actions is not possible without significant modifications to the modeling of the environment. We consider this issue as a topic for future works. Conceptualization, S.C. and A.Z. ; methodology, A.Z. ; software, A.Z. ; validation, S.C. and A.Z. ; formal analysis, A.Z. ; investigation, S.C. and A.Z. ; resources, S.C. and A.Z. ; data curation, A.Z. ; writing--original draft preparation, A.Z. ; writing--review and editing, S.C. and A.Z. ; visualization, A.Z. ; supervision, S.C.; project administration, S.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.