# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Application of Reinforcement Learning Algorithm in the Development of Mobile Robot
#### 4.1.1 Path Planning, Optimization, and Obstacle Avoidance with RL  
Automated mobile robots have widely been employed in the vast majority of working environments, especially in recent times where several work environments have recorded high costs of production. They have varying applications in multiple work environments, including household, agriculture, and industrial operations, for outdoor purposes while exploring resources, and wastewater transportation in the case of indoor surveillance. For the mobile robot to complete a set task, it moves from one point to another while avoiding obstacles and collisions. Path planning is one of the essential technologies of mobile robot functions in obstacle avoidance. Its importance corresponds to its ability to help the robot find the shortest possible route, free of obstacles and collisions. The mobile robot is expected to plan its path to avoid obstacles in outdoor or indoor environments. However, they often encounter problems of obstacles in the process of rotation and transition from the start point to the target endpoint in both known and unknown environments, although the problems are more complex in an unknown environment since there is little or no information available in such an environment. Hence, partial information is sensed using sensors in such an environment, whereas enough information is available for robots to learn about obstacles and plan their path in a known environment. The reinforcement learning (RL) algorithm has been employed to solve mobile robot path planning, optimization, and obstacle avoidance problems. RL frameworks are employed to design a mobile robot, so it frequently interacts with any environment to enhance its adaptability, which, as a result, informs the robot's decision-making. For instance, [PERSON] et al. [28] developed a double deep Q-network (DDQN) approach of reinforcement learning coupled with prioritized experience replay (PER). This was said to provide superior success in path planning and optimization. Their RL method enabled the robot to sense local information, thus planning its path in an unknown environment [29]; the experimental model consisted of a chassis, wheels, and about three ultrasonic sensors that equipped the robot with information to avoid obstacles in an unknown environment. [PERSON] et al. [30] similarly  
Figure 2: Publication trend. modified the deep Q-network (DQN) reinforcement learning theory in a reward-modified approach to achieve an obstacle-free path in a dynamic unknown environment. In their experiment, the DQN reward-modified algorithms enriched the robot to learn an optical decision in an unknown dynamic environment, modifying the abnormal reward function solved to avoid relative movement between the robot and the obstacles leading to abnormal rewards and collision. In this process, two modified reward thresholds were created, and the mobile robot successfully planned its path to avoid a collision. The authors further argued that the reward modified DQN reinforcement learning method offered the best result in mobile robot path planning and optimization of mobile robots in a dynamic unknown environment compared with other DNQ methods. [PERSON] et al. [31] experimented on a known indoor environment. Deep reinforcement learning (DRL) was employed in their experiment to help a mobile robot plan its path in an indoor environment. To better augment the function and address the problems associated with the credibility of the DRL approach, a novel incremental training model was introduced in their experiment in both 2D and 3D environments. The standard global path planning algorithm Probabilistic Roadmap was combined with Twin Delayed Deep Deterministic policy gradients as the novel path planner in the DRL model. And the results of the experiment confirmed that this model of training mobile robots incrementally could enhance the robot's efficiency in planning its path in an indoor environment.