# Autonomous Navigation of Robots: Optimization with DQN
## 2 Related Work  
In this section, a comprehensive analysis is conducted on the most notable research and work pertaining to the design and implementation of control systems based on machine learning. The objective of this research proposal is further described from a technical standpoint, elucidating its potential applicability to diverse control systems and optimization algorithms. By exploring the existing body of knowledge and examining relevant studies, this section aims to establish a solid foundation for the proposed research, fostering a deeper understanding of the subject matter and its implications in the field of control system design. Reinforcement learning (RL) is a type of machine learning that involves an agent interacting with an environment to learn the best actions to take in order to maximize a reward signal. RL has been successfully applied in robotics to solve a variety of tasks, including path determination [17; 18; 19; 20]. Path determination in robotics involves finding a safe and efficient path for a robot to follow in order to complete a task. This can be a challenging problem, as robots need to navigate through unknown environments while avoiding obstacles and minimizing the risk of collisions [21; 22; 23; 24; 25; 26; 27; 28]. RL can be used to train a robot to determine the best path to take in order to achieve its goals [29; 30; 31; 32]. The robot can be trained by interacting with its environment and receiving feedback in the form of rewards or penalties based on the actions it takes. For example, the robot may receive a reward for successfully navigating to a particular location, while receiving a penalty for colliding with an obstacle [33; 34; 35; 36; 37]. Over time, the robot can use the feedback it receives to learn the optimal path to take in different environments. This allows the robot to adapt to changes in its environment and make decisions in real time based on the current situation [38; 39; 40; 41]. Due to technological advances, the research and implementation of robotic systems are in constant development, trying to optimize self-control, leading their system to be based on autonomous operations and intelligent decision making [42; 43]. Especially for movement, different control methods have been designed that vary according to their field of application; however, the most used is the predictive model, which is based on generating a decision based on statistics, which in turn uses a large amount of data in industrial environments [44; 45; 46; 47; 48; 49]. Various investigations [50; 51] indicate that mobile robots are extensively utilized in diverse industrial and commercial settings, often replacing human labor. Instances can be found in warehouses and hospitals, where these robots are responsible for material transportation and assisting workers in repetitive tasks that may have adverse effects on their wellbeing [52; 53]. Additionally, it is observed that in many domains involving mobile robots, the processing of a substantial amount of information poses a significant challenge. Within the context of machine learning, this refers to the learning process taking place within an environment that encompasses both fixed and mobile obstacles. The navigation tasks of mobile robots typically involve various optimization concepts, such as cost reduction, shorter trajectories, and minimized processing time. However, in complex and unpredictable industrial environments, adaptability to surroundings becomes essential [54; 55; 56]. The Deep Q-Network (DQN) algorithm is a popular variant of reinforcement learning that has been successfully applied to a wide range of problems, including path determination in robotics [57; 58; 59]. In DQN, the agent uses a neural network to approximate the optimal action-value function, which maps states to the expected long-term reward for each possible action. The network is trained using a variant of the Q-learning algorithm, where the agent learns from the transitions between states and the rewards received for each action [60; 61; 62].