# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 1 Introduction
### Whats Is known  
#### 1.2.1 Application of Reinforcement Learning Algorithm in Mobile Robot Application  
In recent years, RL has gained significant attention and is widely used in various domains, including robotics. RL algorithms are used to design autonomous robots that can learn and adapt to new environments and tasks. Mobile robotics refers to the design and development of robots that can move autonomously in the physical world. There are several applications for mobile robots, which are logistics and healthcare, as well as agriculture, to perform navigation, object recognition, and manipulation tasks. They are equipped with various sensors and actuators that enable them to perceive their surroundings and interact with the environment. Mobile robots with RL algorithms have demonstrated impressive capabilities in navigating complex environments, avoiding obstacles, and performing tasks accurately. While mobile robot applications have been adopted in most human activities, reinforcement learning algorithms have also been simultaneously utilized to solve several problems and the shortcomings/inefficiency of mobile robot applications. For example, in recent times, mobile robots have found it difficult to navigate autonomously in a dynamic environment since most traditional algorithms are only efficient in a static environment where obstacles are not moving. In such an environment, a mobile robot usually works based on mapped information on the existing obstacles. So, it becomes a major problem for the mobile robot to autonomously navigate in a dynamic environment as it is not equipped to learn to plan its path to avoid dynamic obstacles continuously. Therefore, several techniques have been employed to help mobile robots solve such problems, and reinforcement learning is one of those promising algorithms for mobile robot autonomous navigation. RL algorithms have been applied to mobile robots, which enhance them to learn and evolve to new spaces and tasks. The RL algorithm learns by interacting with the environment, and the robot's behaviors are modified in response to feedback from its surrounding environment. This allows the robot to draw learning from its slips to improve over time. Learning is one of the RL algorithms that is most frequently used in mobile robotics. Mobile robots use the well-known reinforcement learning algorithm Q-learning [4] to discover the best strategies for performing a variety of tasks, including navigation, obstacle avoidance, and object tracking. The goal of Q-learning is to teach the robot the optimum course of action to adopt in a given situation [5]. This method can learn the best policy regardless of the behavior policy (off-policy) and is model-free (it learns the best policy without a model of the environment). By choosing actions that would maximize the projected future rewards, the robot learns to navigate its environment. The most effective action is the one that, out of all the potential actions, has the highest Q value [6]. Q-learning is therefore a value-based approach. To determine the best course of action, the agent uses the greatest expected Q values during the learning process [7]. Q-learning [8] blends temporal difference (TD) learning with theories like the Bellman equations and the Markov decision process (MDP). Agents can learn how to behave best in regulated Markovian domains. One-step Q-learning, which is its form, is what Equation (1) defines. \[\text{Q}\left(\text{s, a}\right)=(1-\alpha)\text{Q}(\text{s, a})+\alpha( \text{r}+\gamma\text{ max}(\text{Q}(\text{s', }\forall a^{\prime}))) \tag{1}\]  
Q(s, a) estimates the action value following the application of an action in state s, where r is the immediate reward received, \(\gamma\) is the discount factor, and \(\alpha\) is the learning rate [9]. The following are the primary components of Q-learning [5]:  
1. Agent: the learner who is equipped with a collection of sensors and actuators to interact with the environment;
2. Environment: the world around an agent or everything that interacts with it;
3.