# Autonomous Navigation of Robots: Optimization with DQN
## 7 Conclusions and Future Work  
The article presents a novel control algorithm based on a DQN network, which has been designed as a node through the ROS API. The architecture proposed for this case study integrates different open source systems that are represented as the agent and the environment, and they are interrelated through states, actions, and rewards to learn through reinforcement. The experimentation conducted in this research allows for the optimization of the functional structure of the neural network by correcting some of its parameters. It is determined that the RMSprop optimizer and MSE loss function are the most appropriate for the calculation of regression, which helps the robot to find the goal more accurately and avoid collisions better, leading to better performance at the reward level.  
The findings of the present study contribute to the development of robotics platforms by proposing a new control algorithm that shows better performance compared to other existing approaches. The proposed DQN-based algorithm provides a promising solution for the control of robotic platforms by learning from interactions with the environment. Collisions are better, thus giving better performance at the reward level.  
While DQN algorithms have shown promising results for path navigation and obstacle avoidance in robotic platforms, there are still some limitations and gaps in their use. One of the main challenges is the high computational resources required for training DQN networks, which limits their real-time application on resource-limited robotic platforms. Additionally, DQN algorithms require a significant amount of training data, which can be time-consuming and expensive to collect in real-world scenarios.  
Another challenge is the sensitivity of DQN networks to changes in the environment or task, which can lead to overfitting or poor generalization. This can make it difficult to transfer the learned policy to different environments or tasks, which limits the versatility of the approach.  
Furthermore, DQN algorithms often require a carefully selected set of hyperparameters, and tuning these parameters can be a challenging task. Moreover, the optimal configuration of hyperparameters for a specific task may not generalize well to other tasks, which makes it difficult to use DQN algorithms for a wide range of applications. Finally, while DQN algorithms have shown impressive results for some tasks, there is still a need for more comparative studies with other reinforcement learning algorithms to determine their performance advantages and limitations in different applications.  
Further research should focus on exploring the scalability and computational efficiency of the proposed algorithm in more complex environments and compare its performance with other approaches to identify the most effective solution for path detection in robotics platforms.  
Conceptualization, J.E.-N. and M.V.G.; methodology, P.A.; software, J.E.-N., G.C. and C.A.G.; validation, M.V.G.; investigation, J.E.-N., E.J. and M.V.G.; writing-original draft preparation, J.E.-N. and M.V.G.; writing-review and editing, J.E.-N., C.A.G. and M.V.G.; visualization, P.A.; supervision, M.V.G.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project PFISEI32.  
The authors declare no conflict of interest.