# 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
Policy: a mapping between the set of seen states, S, and the set of actions, A;
4. The reward function, which maps state-action pairs to a scalar value;
5. Q value: the total payment that an agent might anticipate accumulating over time, starting from that state. Learning functions by incrementally increasing its assessments of the effectiveness of activities at specific stages [7]. Similar to [PERSON]'s approach of temporal differences, learning involves an agent trying an action at a specific state and evaluating the results in terms of the immediate reward or penalty it receives as well as its estimation of the worth of the state from which it was taken. Then, it discovers which actions--as determined by long-term discounted reward--are generally the best by repeatedly attempting all states and actions [10]. A series of unique stages or incidents make up the agent's experience. The agent in the nth episode [11]  
1. Notices its current state s\({}_{\text{t}}\);
2. Chooses and takes action a\({}_{\text{t}}\);
3. Monitors the subsequent state s\({}_{\text{t}}+1\);
4. Receives a payout right away r\({}_{\text{t}}\). Then, in accordance with [9], the agent modifies its Q values using a learning rate \(\alpha\) Please take note that Q\({}_{\text{m}\times\text{n}}\)(s\({}_{\text{t}}\), a\({}_{\text{t}}\)) is assumed to be represented by a look-up table in this explanation. Learning and performance characteristics are the two core components of a learning agent [12]. What kind of performance element is utilized, which functional component is to be taught, how that functional component is represented, and the type of feedback available all influence the design of a learning element [5]. In mobile robotics, Q-learning can be applied in various ways depending on the task. For instance, in navigation tasks [6], the robot can learn to select the optimal path to reach a target location by evaluating the expected reward of each possible action (such as moving forward or turning left/right) in each state (that is, the robot's current location as well as the orientation). Similarly, in obstacle avoidance tasks [11], the robot may learn to avert obstacles by evaluating the expected reward of each possible action (such as moving forward, turning, or stopping) in each state (such as the robot's proximity to the obstacle and its orientation). To implement Q-learning in mobile robots, one needs to define a suitable state representation that captures relevant information about the robot's environment and a set of possible actions the robot can take [12]. The Q value is then updated based on the rewards obtained by the robot as it interacts with the environment, using the Bellman equation [12]. Another RL-based algorithm used in mobile robotics is deep reinforcement learning (DRL) [9]. DRL uses a deep neural network to approximate the Q values. DRL has been shown to be effective in complex tasks such as object recognition and manipulation [13]. Mobile robots are also trained to carry out tasks like object detection and grasping using DRL-based algorithms [14]. In recent years, deep reinforcement learning (DRL) has grown to be one of the areas of artificial intelligence that has attracted the most attention [15]. Through high-dimensional perceptual input learning, it directly regulates agents' behavior by combining the perception of deep learning (DL) and the capacity for decision-making of reinforcement learning (RL). It offers a fresh concept for resolving robot navigation issues. One of the main advantages of RL-based algorithms in mobile robotics is their ability to adapt to new environments and tasks [16]. The robot learns by interacting with the environment, and its behavior is adjusted depending on the feedback the environment has provided. As a result, the robot may learn new situations and activities without the need for manual programming. Mobile robots' RL-based algorithms, however, encounter several challenges. The high-dimensional state space is one of the difficulties [17].