## 4 Hybrid Algorithm Based on DQN and DWA
### Improved Hybrid Algorithm Simulation Analysis  
Aiming at the proposed algorithm, this paper makes a comparative test of the following five algorithms:  
1. As shown in Figure 26, the path planning times of A*, DLN, DQN, DDPG and Hybrid A* in the same environment are compared. 2. As shown in Figure 27 the number of A*, DLN, DQN, DDPG and Hybrid A* path nodes in the same environment are compared. 3. As shown in Figure 28, in the same environment, A*, DLN, DQN, DDPG and Hybrid A* successfully reached the target point comparison. Five groups were set up in the comparison experiment, which were carried out independently in the same environment (Figures 26-28). The five sets of experimental results show that, compared with the other four methods, the hybrid A* algorithm significantly reduces the path planning time and the number of nodes, and improves the precision. The performance comparison of the five algorithms includes path planning time, number of nodes, and accuracy, as shown in Table 5.  
Figure 27: A*, DLN, DQN, DDPG, Hybrid A* Comparison of the number of nodes in path planning. Figure 26: A*, DLN, DQN, DDPG, Hybrid A* Comparison of path planning time. The selected values in Table 5 above are the average values of the five groups of experimental results, and the optimization rate is a mix of A* compared with other algorithms. From Table 5, the planning time of the mixed A* algorithm is shortened by 26.28%, the number of nodes is reduced by 40%, and the accuracy is increased by 20% compared with the A* algorithm in the same environment. Further, this paper also uses the number of training steps of the algorithm plot to evaluate the performance of the training process of the above two algorithms, and the results are shown in the figure below. In Figure 29, the horizontal coordinate represents the number of algorithm training iterations, and the vertical coordinate represents the number of algorithm training steps (step). As Figure 29 shows, in the early training period, the DQN algorithm had a high number of training steps during 0-60 iterations. The convergence effect of the algorithm was not obvious during 0-60 iterations, and the network model oscillations occurred near sections 80, 100 and 120 of the algorithms. If the environment model was more complex, the algorithm could not converge and could not complete the path planning. As Figure 29 shows, the improved hybrid algorithm has a high number of training steps (between 0 and 45 iterations) at the initial stage of training, and the convergence effect of the algorithm after 45 sections is more obvious. The algorithm will converge rapidly even if there is a short oscillation and tends to be flat, and almost no network model oscillation occurs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{\begin{tabular}{c} **Path** \\ **Planning** \\ **Time** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Optimization** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Path** \\ **Planning** \\ **Node** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Optimization** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Sucessful** \\ **Arrivals** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Optimization** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} } \\ \hline A* & 0.9428 & 26.28\% & 10 & 40\% & 10 & 20\% & 5 \\ \hline QLN & 0.8694 & 20.68\% & 8 & 25\% & 10 & 20\% & 5 \\ \hline DQN & 0.8331 & 16.61\% & 8 & 25\% & 11 & 9.09\% & 5 \\ \hline DDPG & 0.8163 & 14.95\% & 8 & 25\% & 10 & 20\% & 5 \\ \hline Hybrid A* & 0.695 & - & 6 & - & 12 & - & 5 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of algorithm performance indicators. Figure 28: A*, DLN, DQN, DDPG, Hybrid A* Comparison of the number of times the target point was successfully reached.