## 5 Simulations Results and Discussion
Moreover, the proposed scheme started convergence when the number of tasks exceeded 90. \begin{table}
\begin{tabular}{l l} \hline Number of E-APs & 10 \\ \hline Number of users \((K)\) & 10 \\ \hline Power noise \((\sigma^{2})\) & \(-174\) dBm \\ \hline Maximum transmit power & 23 dBm \\ \hline Bandwidth \((W)\) of channel & 20 MHz \\ \hline Pathloss model & \(128+37*\log_{10}(\text{distance})\)[40] \\ \hline Experience replay buffer size \(N_{D}\) & 2000 \\ \hline Learning rate \(\alpha\) & 0.01 \\ \hline \(\varepsilon\)-greedy & 0.9 \\ \hline Reward decay \(\xi\) & 0.9 [29] \\ \hline Mini-batch size \(M\) & 32 \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental parameters. Figure 5 depicts the performance of the number of computing resources with the offloading energy consumption. This analysis considers a scenario with 20 E-Aps with a processing capacity of 1.5 GHz each. The proposed approach achieved the lowest energy consumption from the schemes examined because of the DRL's remarkable controlling capabilities. Moreover, the DRL-CORA scheme exhibited superior performance compared to both the Q-learning and fixed approaches. In addition, the Q-learning and fixed approach saved nearly the same energy and showed almost the same trend in Figure 5. On the other hand, the random approach acquired more energy due to its random decision and exploitation behavior. The energy consumption associated with tasks with varying completion time constraints in the application request is illustrated in Figure 6. As depicted in Figure 6, tasks with more lenient completion time constraints resulted in lower energy consumption. The graph indicates that, after a certain threshold, the time constraint results we extended for a similar offloading energy consumption for all methods. This phenomenon can be attributed to the less stringent task requirements and sufficient availability of resources, resulting in similar task assignments across all methods. Therefore, the energy consumption across different methods was highly similar. It is imperative to highlight that our proposed scheme consistently outperformed the other methods, particularly across the entirety of task completion time constraints. This superiority is particularly evident when the completion deadline surpassed 20 ms, confirming our algorithm's rapid convergence. This further underscores the robustness and effectiveness of our proposed approach in optimizing energy consumption. Figure 4: The evaluation of computing energy versus number of computational tasks. Figure 3: Evaluation of offloading energy consumption under different modes. As illustrated in Figure 7, interference can also influence the offloading decision and may be influenced by the decisions of other users. Figure 7 highlights the significance of the active user count within the network. As the quantity of users inside the network expanded, there was a corresponding increase in interference, leading to a reduction in the signal-to-interference-plus-noise ratio (SINR) and subsequently a decline in the data rate. This led to higher latency and energy consumption. Therefore, the proportion of local users (not necessarily the exact quantity) also increased. Figure 7 depicts the comparison conducted to examine the influence of interference. For a small number of users, the level of interference was insignificant due to the lower level of interference imposed by others and, therefore, it had no effect on the overall solution. As the quantity of users escalated, the impact of interference became increasingly substantial and could not be ignored. Therefore, considering an interference-free scenario can lead to incorrect decisions.Thus, our proposed solution outperformed the other approaches for all numbers of users. Figure 5: The evaluation of computing energy versus number of E-APs. Figure 6: The evaluation of computing energy versus task completion deadline.