## 5 Numerical Results  
In this section, we present the numerical simulation results to illustrate the performance of the proposed two offloading paradigms. Assume that the time interval of the system is 1 ms, and the bandwidth of the intelligent fault diagnosis system is 1 MHz. Additionally, the required CPU cycles per bit are 300 cycles/bit at the intelligent terminals and 120 cycles/bit at MEC servers. In the training process, the learning rate of the DQN-based offloading algorithm is 0.01. In the DDPG-based offloading algorithm, the learning rate of the actor network is 0.001, and the learning rate of the critic network is 0.001. In Figure 7, we plot the training process of the DQN-based algorithm and the DDPG-based algorithm, 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. The delay of the system is in an unstable state with large fluctuations in the beginning, indicating that the agent is constantly exploring the environment randomly. After a period of learning, the delay decreases slowly, and the fluctuation range gradually gets smaller. After about 1200 iterations, the DDPG-based algorithm converges to a stable value of 1.2; after about 1500 iterations, the DQN-based algorithm converges to 1.22. At this time, the average reward of each episode no longer changes, and the training process is completed. The DDPG-based algorithm converges faster and can obtain a lower latency than the DQN-based algorithm. This indicates that the performance of the DDPG-based algorithm is better than the DQN-based algorithm for our offloading problem. In Figure 8, the DDPG-based computational offloading paradigm, the DQN-based computational offloading paradigm, and the \"Random\" policy are compared for different task sizes in terms of delay. \"Random\" means that the computing resources are allocated randomly. The delay difference between the three policies is slight at task sizes below 2.5 bit, with the DDPG-based computational offloading paradigm having the smallest delay and the \"Random\" policy having the largest delay. The delay of the \"Random\" policy increases the most as the task size increases, while the latency of the DDPG-based computational offloading paradigm and the DQN-based computational offloading paradigm increases slightly less. The delay of DDPG's computational offload paradigm and DQN-based computational offload paradigm consistently remains low compared to the \"Random\" policy. Figure 6: The structure of the DDPG-based offloading algorithm. In Figure 9, we illustrate the offloading delay as the function of the amount of tasks at intelligent terminals. Three benchmarks, namely \"Random\", \"Local computing\", and \"MEC server computing\", are chosen to compare the performance with the proposed two offloading paradigms. Here \"Random\" means that the computing resources are allocated in a random manner; \"Local computing\" and \"MEC server computing\" mean that the tasks are processed only at intelligent terminals and only at MEC servers, respectively. The curves in Figure 9 show that the required time delay increases correspondingly as the amount of tasks grows. The computation delay of \"Local computing\" is the largest as intelligent terminals have little local computing capacity. \"MEC server computing\" performs better than \"random scheme\" when the task arrival rate is more significant than 2.7 Mbps, which indicates that when the task arrival rate increases, task offloading to MEC servers can obtain a lower time delay. When the task arrival rate is greater than 4 Mbps, the offloading time delay of \"MEC server computing\" is close to the DQN-based offloading paradigm. Figure 8: The delay comparison under different task sizes, 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, and the yellow curve represents the delay of the “Random” policy.