# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Application of Reinforcement Learning Algorithm in the Development of Mobile Robot
#### 4.1.2 Navigation and Learning with RL  
One of a mobile robot's fundamental features is autonomous navigation. It is without a doubt essential to robotic operations. It tends to navigate in an environment with a static or dynamic obstacle to hit its target through a thorough comprehension of its navigating environment in order to move from its set point to its goal. However, the efficiency and accuracy of the robot depend on a good perception of its environment and learning to navigate within the shortest possible time. This seems to be one of the leading problems in mobile robot development, especially with a less efficient traditional algorithm in mobile robot navigation. This traditional algorithm works based on a previous obstacle map in a navigation environment. Despite the high accuracy and short path of the algorithm, establishing the obstacle map is lengthy and, in some conditions, may not be available. Also, the accuracy of the algorithm depends on the precision of the map range. However, the emergence of the reinforcement learning algorithm has helped solve several problems that limited mobile robot navigation. RL offers a proper understanding of known and unknown environments through learning easy robot navigation without encountering obstacles. Several experimental interventions adopting different reinforcement learning algorithms have been developed and tested for their possible success in navigation. As opposed to the traditional navigation algorithm, [PERSON] et al. [39] develop and test a path-planning for nonholonomic robots that takes into account a moving/stationary obstacle avoidance approach which is achieved by integrating a chaotic metaheuristic optimization technique with a single-time velocity estimator-based reinforcement learning RL algorithm. The study further contributes by incorporating the impacts of tire sinkage into the deformable terrain on the dynamics of the robot and applying the principles of Terr mechanics to determine the ideal compensatory force/torque magnitude to support a stable and fluid motion. Through continuous interaction with the navigating environment, reinforcement learning models enable mobile robots to learn and navigate safely in any environment compared with supervised learning. Another reinforcement learning model was tested by [PERSON] and [PERSON] [40]. The experiment found that the Q-learning algorithm of the RL method successfully helped the mobile robot avoid an obstacle in a navigation environment through alternative path planning different from the prior established optimal path used in most traditional algorithms. The study posited that using reinforcement learning techniques or methods made it easy for mobile robots to navigate dynamic obstacle environments. Several scholars also intervened in the problem of autonomous learning of conventional or traditional algorithms used for mobile robot development using deep reinforcement learning (DRL). This was confirmed by [PERSON] et al. 's [41] experiment reviewed by this paper. Similar to [38], they used an end-to-end deep reinforcement learning algorithm to solve the problem of autonomous mobile robot navigation in an unknown environment. A combination of dueling network architecture for RL (Dueling DQN) plus DRL with double q learning (DDQN) to form a D3 QN algorithm allowed the mobile robot to autonomously learn about the environment in a gradual process while transiting and learning to navigate its path to the target goal with an RGB-D camera in the environment. Like previous authors, the study also demonstrated that adopting the RL model in mobile robot development allowed the robot to reach its goal without colliding with obstacles. To address the problems that are instances of local stable points, an improved black-hole potential field and reinforcement learning can be employed [42]. In a reinforcement learning algorithm, the environment is the black-hole potential field. Agents automatically adjust to their surroundings and learn to use the most basic environmental data to locate targets.