# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 1 Introduction
### Whats Is known
#### 1.2.2 Description of Terms  
* Mobile Robot  
Mobile Robot is a unique robot designed to move around and perform tasks in different environments without being physically fixed to one spot [4]. Instead, these robots have a mobility mechanism that enables them to move around on their own or with human control, such as wheels, tracks, or legs. Applications for mobile robots include exploration, transportation, manufacturing, and healthcare [2]. In addition, they can be programmed to carry out activities like object manipulation, delivery, surveillance, and inspection [2]. [PERSON] [4] discussed that mobile robots can sense and navigate their environments using various sensors, including cameras, lidars, sonars, and microphones. In addition, they use software and control algorithms to decide, plot paths, avoid obstacles, and communicate with people or other robots [9]. Furthermore, most mobile robots can learn from their surroundings and enhance their performance over time owing to artificial intelligence and machine learning [4]. These cutting-edge features make mobile robots incredibly versatile and adaptive to different tasks and environments. * Reinforcement Learning  
Reinforcement learning (RL) [2] reveals how natural and artificial systems can learn to predict the results of their actions, optimize those actions in environments where they might result in rewards or punishments, and move them from one state or situation to another. [PERSON] et al. [12] described RL as a subset of machine learning that focuses on teaching algorithms how to maximize a reward signal to make decisions in dynamic situations. It uses a procedure in which an agent interacts with its surroundings and is given feedback through rewards or punishments for its activities [12]. As a result, the agent learns optimal decision-making through trial and error by exploring the environment and taking actions that lead to the highest possible reward [12]. RL is especially well suited for use on mobile robots. This is because RL enables mobile robots to learn from their environment and adapt to changing conditions, which is necessary for autonomous navigation and decision-making; this is confirmed by [6]. RL algorithms can also enhance the operational efficiency of mobile robots by determining the best course of action in real-time [18]. Another author [9] discussed that RL could be used for various mobile robot applications, such as autonomous navigation, object recognition and manipulation, and task execution. Complete independence from human labeling is the key advantage of reinforcement learning [2]. Furthermore, reinforcement learning is a promising strategy for creating autonomous systems that can adapt to changing environments and learn from their own experiences because the estimation of the action-value function is self-driven by the trial-and-error interaction with the environment and takes the robot states as the input of the model [6]. However, the training process can be time-consuming and requires careful tuning of hyperparameters to achieve optimal performance. Mobile robots have to go through a complex environment, dodge hazards, and eventually choose the optimal path. DRL has recently made some advancements in the labyrinth and indoor navigation. The A3C algorithm's performance in maze navigation was proposed to be enhanced by employing unsupervised auxiliary tasks by [PERSON] The proposed algorithm increased the convergence speed, resilience, and success rate. [PERSON] presented a DQN [21] and visual serving-based path-planning technique for mobile robots. To accomplish indoor autonomous navigation, the initial environment and the target image were collected as inputs, and matching relations and control techniques were formed through training. With the use of navigation technology, the development of navigation technology is becoming increasingly important for the development of robot path-planning technology.