# Autonomous Navigation of Robots: Optimization with DQN
## 2 Related Work
To apply DQN to path determination in robotics, the agent must first be trained on a set of sample environments. During training, the agent explores the environment and learns to predict the optimal action to take in each state. The agent's performance is evaluated based on its ability to navigate to a specified target location while avoiding obstacles [63; 64; 65]. A novel work is presented in [54], where reinforcement learning is applied; it is mentioned that the robot will learn by training the environment in which it is located through a scoring system designed in a Deep Q network, which later will allow it to take the optimal action or strategy to move to its target position while avoiding obstacles. Also in [67], it is mentioned that laser sensors are used for the autonomous navigation of mobile robots because they have a wide detection range and high precision; in addition to this research, it is detailed that robots need enough information to correctly detect obstacles and map the environment to subsequently carry out correct navigation. Programming interfaces have been developed for the design and control of open-source applications, owing to the changes in computer systems and the incorporation of robotics into the industry [68; 69]. ROS has the stability to program any type of robotic function, either for a manipulator or a mobile robot; in [70], this framework is used to control the entire flow of information through the tools and libraries included in its API, which contains functional packages that facilitate the creation and communication between nodes. As mentioned in [71], a successful application developed between big data and machine learning can provide different solutions for solving problems in manufacturing industries, especially in the product life cycle and the entire supply chain. Also in this research, a model based on energy saving is presented using machine learning to determine the optimal trajectory for an industrial robot; the keys to this design are based on data collection and the control algorithm. For mobile robots, autonomous navigation based on machine learning is the key to high-impact technological advancement; several works [72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88] indicate that industrial robots that have the appropriate sensors and the specific control algorithm can understand the environment in which they are located, to which humans generally do not have access, such as in war zones or nuclear plants. The research by [89] shows three different control algorithms based on different techniques; in addition to the development of a simulation environment for the training of the mobile robot, a function focused on Q-learning was also introduced to return the reward value according to the records of executions and thus validate the performance of the robot. The Q-values are reported to be real, which turns the neural network into a regression task, allowing for optimization using the squared error loss function. Overall, the combination of reinforcement learning with the DQN algorithm has shown great promise in enabling robots to autonomously navigate through complex environments and find safe and efficient paths to their goals. As can be seen in this section, progress in Industry 4.0 has led to the development of new technologies applicable to mobile robots, allowing for the design of control systems based on autonomous learning, resulting in more efficient devices due to the trajectory obtained by the optimized algorithm. This work proposes a mobile robotic system trained by means of simulation, capable of avoiding obstacles and being optimal in tracking trajectories.