# An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning
## 5 Experimental Analysis
### Simulation Comparison Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Success Rate** & \
\begin{tabular}{c} **Convergence** \\ **Average Steps** \\ \end{tabular} &
\begin{tabular}{c} **Convergence** \\ **Average Time/s** \\ \end{tabular} \\ \hline DQN & 9.5\% & 357 & 174 \\ Duel DQN & 27.7\% & 144 & 70 \\ PMR-DQN & 46.3\% & 88 & 43 \\ PER-D2 MQN & 52.7\% & 77 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of four algorithms in the scenario (b). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Success Rate** & \
\begin{tabular}{c} **Convergence** \\ **Average Steps** \\ \end{tabular} &
\begin{tabular}{c} **Convergence** \\ **Average Time/s** \\ \end{tabular} \\ \hline DQN & 13.6\% & 161 & 78 \\ Duel DQN & 36.2\% & 94 & 45 \\ PMR-DQN & 57.6\% & 74 & 38 \\ PER-D2 MQN & 65.8\% & 64 & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of four algorithms in the scenario (a). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Success Rate** & \
\begin{tabular}{c} **Convergence** \\ **Average Steps** \\ \end{tabular} &
\begin{tabular}{c} **Convergence** \\ **Average Time/s** \\ \end{tabular} \\ \hline DQN & 0.2\% & — & — \\ Duel DQN & 12.2\% & 238 & 116 \\ PMR-DQN & 23.8\% & 167 & 81 \\ PER-D2 MQN & 44.6\% & 91 & 44 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of four algorithms in the scenario (c). In scenario (b), the PER-D2 MQN algorithm maintains the best performance with a success rate of 52.7%, an average number of steps of 77, and an average time consumed of 38 s, despite the influence of dynamic obstacles. Facing a more complex and variable scenario (c), the PER-D2 MQN algorithm has a 44.6% success rate, which is decreased but significantly better than the other algorithms, especially close to twice the success rate of the PMR-DQN algorithm. In contrast, the remaining three algorithms perform significantly worse in scenario (c). Notably, the PER-D2 MQN algorithm shows relatively small variations in performance parameters across the three scenarios, demonstrating its greater adaptability in complex environments. In addition, for the DQN algorithm in scenario (c), the results are still counted in the calculation of the average number of steps and the average time due to the fact that only one episode succeeded in finding the target point, which is subject to a certain degree of chance. These data further demonstrate the robustness and superiority of the PER-D2 MQN algorithm in multi-scene tasks. Through knowledge transfer learning, previously trained neural network weights are migrated to new training scenarios, thereby accelerating the convergence process and reducing resource consumption. Using a priori knowledge to initialize the network parameters instead of random initialization can significantly improve the performance of reinforcement learning methods in obstacle avoidance tasks and help generate better paths [26; 37; 38]. Figure 12 illustrates the cumulative reward performance of the PER-D2 MQN algorithm with or without applying knowledge transfer learning conditions, where (a) represents the cumulative reward when the robot is directly trained for 500 episodes in scenario (b), and (b) represents the cumulative reward performance when the robot is trained for 500 episodes in scenario (a), then migrates the learned knowledge to scenario (b), and is further trained for 500 episodes. From Figure 12, it can be seen that after applying knowledge transfer learning, the cumulative reward quickly converges to 8000 after 130 episodes, showing high convergence efficiency. In contrast, without using knowledge transfer learning, the cumulative reward fluctuates between 4000 and 8000 after 200 episodes, with a slower and unstable convergence trend. This suggests that the robot, with prior knowledge of scenario (a), can significantly improve its performance in the training of scenario (b), accelerating the learning process and obtaining better results.