# An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning
## 5 Experimental Analysis
### Simulation Comparison Experiment
The PMR-DQN algorithm demonstrates better stability, with its average reward gradually converging to approximately 5200 after 260 episodes, while its cumulative reward fluctuations are smaller compared to Duel DQN. Notably, the PER-D2 MQN algorithm achieves the best convergence performance, with its average reward stabilizing at around 6200 after 300 episodes. Moreover, it has the smallest cumulative reward fluctuations, indicating significantly improved performance over the other algorithms. The comparison of average rewards in Figure 11b further highlights the superiority of the PER-D2 MQN algorithm, as its average reward is consistently higher than those of the other algorithms. As shown in Figure 10, in the more challenging scenario (c), the DQN algorithm struggles to efficiently locate the target point. Throughout the entire training process, it successfully avoids all obstacles and completes the task in only one episode. In contrast, the other three algorithms demonstrate the ability to complete the task multiple times in this complex environment, with the PER-D2 MQN algorithm exhibiting the most remarkable performance. In terms of average rewards, the Duel DQN algorithm begins to converge after 218 episodes, with its average reward stabilizing at approximately 2300. This indicates that the robot performs many inefficient actions while searching for the target point, leading to the identification of only a few target points within a single episode. In comparison, the  
Figure 11: Comparison of average rewards of four algorithms. (**a**) is the average reward comparison in scenario (**a**); (**b**) is the average reward comparison in scenario (**b**); (**c**) is the average reward comparison in scenario (**c**). PMR-DQN and PER-D2 MQN algorithms significantly reduce inefficient actions, enabling the robot to locate more target points. Specifically, the PMR-DQN algorithm achieves an average reward of around 4000 after 300 episodes, while the PER-D2 MQN algorithm stabilizes its average reward within the range of 4800 to 6000. Furthermore, Figure 10(c) provides a more intuitive comparison, clearly showing that the average reward of the PER-D2 MQN algorithm is significantly higher than that of the other three algorithms. This demonstrates that, after 500 training episodes in scenario (c), the PER-D2 MQN algorithm is not only more efficient at locating target points but also achieves higher rewards per episode, highlighting its distinct advantages. To further demonstrate the superiority of the PER-D2 MQN algorithms, we evaluated the performance of the four algorithms in three scenarios trained with 500 episodes in detail. The evaluation metrics include success rate (the percentage of episodes in which the robot avoids collisions), average steps (the mean number of steps required for the robot to reach the target point in all successful episodes), and average time (the mean time required for the robot to locate the target point in all successful episodes). These metrics comprehensively demonstrate the performance advantages of the PER-D2 MQN algorithm from multiple perspectives. Tables 3-5 show the performance parameters of the four algorithms in three scenarios, respectively. It can be clearly seen that the PER-D2 MQN algorithm outperforms the other algorithms in all the scenarios. In scenario (a), its success rate reached 65.8%, which was the highest among all algorithms; the average number of steps was 64 and the average time consumed was about 31 s, which were both the lowest. In contrast, the DQN algorithm and the Duel DQN algorithm had success rates of 13.6% and 36.2%, respectively, neither of which exceeded 50% and required significantly more steps and time to complete the task. The PMR-DQN algorithm, with a success rate of 57.6%, had a relatively small number of steps and time consumed, and performed second to the PER-D2 MQN algorithm.