## 5 Numerical Results
Figure 7: The delay dynamics of each iteration of the DQN-based and DDPG-based algorithms during the training process, where the blue curve represents the delay dynamics of the DQN-based algorithm and the red curve represents the delay dynamics of the DDPG-based algorithm. computation offloading algorithm, indicating that most tasks are offloaded to the MEC servers with large task sizes. Both proposed DQN and DDPG offloading paradigms achieve better performance than other benchmarks, proving the proposed methods' effectiveness. On the other hand, the DDPG-based computation offloading paradigm achieves a lower computation delay than the DQN-based computation offloading paradigm, which further verifies the superiority of the DDPG algorithm in dealing with high-dimensional continuous action-state space problems. Figure 10 shows the impact of the computing capabilities of intelligent terminals and MEC servers on the processing delay. We fix the local computing capability as a constant value and increase the computing capacity of the MEC server continuously, so the computation delay of \"Local computing\" is not affected by the ratio of computing capacity between the intelligent terminal and MEC server. Under different computing capabilities, the proposed DQN and DDPG offloading paradigms can achieve better performance than the other three benchmarks, and the performance of the DDPG-based offloading paradigm is slightly better than the DQN-based offloading paradigm. When the ratio of MEC server computing capacity to intelligent terminal computing capacity locates between two and three and the ratio increases, the processing speed of the MEC server is faster than the intelligent terminal, and the intelligent terminal chooses to offload more tasks to the MEC server. The computation delay of \"MEC computing\" is smaller than the \"random scheme\". When the ratio exceeds three and as the ratio increases, the processing speed of the MEC server is significantly higher than the intelligent terminals. The intelligent terminal prioritizes the task offloading, and the task processing delay is still decreasing, but the downward trend slows down. The computation delay of \"MEC computing\" is lower than the \"random scheme\" and close to the DQN-based offloading paradigm, which indicates that most or all tasks are offloaded to the MEC servers. The decrease in the delay is mainly due to the increase in the computing capacity of the MEC servers. Figure 11 illustrates the computation delay under different energy constraints at the intelligent terminals. The curves show that \"MEC computing\" is not affected by the change in energy of the intelligent terminal. \"Local computing\" highly depends on the intelligent terminal energy constraint, and the computation delay decreases significantly as the intelligent terminal energy increases. The increase in intelligent terminal energy indicates a fast local processing speed and high available transmission at the intelligent terminals, which can reduce the computation delay to a certain extent. The computation  
Figure 9: The delay comparison under different task arrival rates, where the red curve represents the delay of the DDPG-based computational offloading paradigm, the blue curve represents the delay of the DQN-based computational offloading paradigm, the yellow curve represents the delay of the “Random” policy, the purple curve represents the delay of the “Local computing” policy, and the green curve represents the delay of the “MEC server computing” policy. delay of DQN-based and DDPG-based offloading paradigms decreases significantly as the intelligent terminal's energy increases at the beginning. The computation delay gradually decreases when the intelligent terminal's energy reaches a certain level, which shows that the intelligent terminal's energy constraint significantly impacts the computation delay within a specific range. The computation delay has a weaker impact when the intelligent terminal's energy exceeds a certain range.