## 4 Experimental Evaluation and Discussion
### Experiment and Discussion on the Automatic Coordination of Resource Planning
In order to achieve the goal of an optimal solution, the paper proposes the PERT-RP-DDPGAO algorithm, which allows machines to solve the optimal solution of planning and coordination through self-learning. The PERT-RP-DDPGAO algorithm converts the impact of plan adjustments and the matching of resources and plans by managers into time parameters, resource planning matrices, and reward functions. By using this method, the computer can obtain the optimal solution through rigorous calculations. Of course, computers are also affected by the accuracy of input data, the operating environment, system interfaces, and other factors during calculations, resulting in biased results and risks. Therefore, the output results require manual verification by managers. The results of retesting can only be used for actual production. The computer-aided production scheduling method enables managers to make more uni  
Figure 7: Comparison between the manager processing results and the intelligent algorithm processing results. (**A**) represents conflicting resource plans. (**B**) represents the result of the management personnel’s overall planning of (**A**). (**C**) represents the optimization result of the computer algorithm on (**A**). fied and scientific decisions, gradually reducing human uncertainty factors. This is also a requirement for the development of modern enterprises. The final core of the PERT-RP-DDPGAO algorithm lies in the DDPG algorithm section. The DDPG algorithm part is the key environment for implementing the planning and coordination function. This paper aims to further validate the value of the engineering application of DDPG. We conduct comparative experiments between the DDPG algorithm, the DQN algorithm, the greedy search algorithm, and the random search algorithm. This paper first chooses the DQN algorithm because some of the principles of DDPG are the same as those of DQN, but the actor-critic algorithm is added as the basic framework. It is hoped that the superiority of DDPG in continuous control problems can be further verified through new experimental objects. Moreover, we verify whether this algorithm can be used in resource planning and coordination application scenarios, such as whether the response speed is better. Then, heuristic algorithms are considered for comparison to verify the advantages of reinforcement learning in handling such control problems. This paper compares the convergence and response speed of the four algorithms mentioned above, forming Figures 8 and 9. The comparison results show that the DDPG algorithm outperforms the other algorithms in terms of convergence and response. The DQN algorithm is superior to heuristic algorithms. First, this paper briefly explains that the DQN algorithm deep Q-network can be used to solve continuous state space problems. The uniqueness of the DQN algorithm  
Figure 8: Convergence comparison of four algorithms. Figure 9: Comparison of the responses of the four algorithms. lies in the experience replay and the target network. When training the Q-network, experience replay can break the correlation between data and make the data independently distributed, thereby reducing the variance of parameter updates and improving the convergence speed. The use of the target network can alleviate the problem of overestimation to a certain extent and increase the stability of learning. In the application scenario of resource planning, DDPG has more advantages than DQN. In other words, DDPG has its own uniqueness in continuous control problems. The response time is an important indicator for measuring algorithm efficiency. This paper extracts the average response time values of the DDPG algorithm, the DQN algorithm, the gray search algorithm, and the random search algorithm, shown in Table 2. This table shows that the average response time of the DDPG algorithm is greater than that of the other algorithms. The DDPG algorithm has a response time 3.0% lower than that of the DQN algorithm.