# 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
[PERSON] and [PERSON] [32] developed an RL algorithm based on path-planning techniques for usage in a warehouse. In order to minimize information processing delays, the study demonstrated how reinforcement learning algorithms could increase mobile robot path search accuracy and decrease path search time. The authors also highlighted the usage of Q-learning and Dyna-Q-learning reinforcement learning algorithms to create a single agent and optimize its single path. However, the author described how optimizing the warehouse path in an open field may be difficult as it exists in a complex environment, as the dynamic environment changes occasionally. The author also argued that adopting the single-agent and single-path algorithm in an outdoor environment such as a warehouse was insufficient. Several other RL methods have demonstrated success in helping mobile robots avoid obstacles. [PERSON] et al. [33] mentioned that continuous space path planning using reinforcement learning alone was enough to help mobile robots plan the path and avoid obstacles. Compared with multiple traditional algorithms, this method showed a high success rate. Similarly, [PERSON] et al. [34] highlighted how several deep reinforcement learning algorithms, such as Deep Q-Network and Double Deep Q-Network combined with Prioritized Experience Replay, could successfully achieve a collision-free path. These frameworks sense information from the environment to autonomously help mobile robots plan their path to avoid obstacles while performing activities. This finding was similar to that of [28], which also found RL in obstacle avoidance using the same approach. Moreover, several studies have described how complex it is for a mobile robot to plan its path in a dynamic environment compared with a static obstacle environment where information about the environment is fed into the mobile robot, just as the traditional algorithms use obstacle maps. [PERSON] et al. [35] suggested that an improved Q-learning algorithm in a modeled environment, a reward function, and policy adaptation helped mobile robots plan their path, avoid obstacles, and hit the target goal via the fastest possible route in a dynamic environment where no fixed information about obstacles existed. The findings were congruent with those of [PERSON] et al. [36], who also claimed mobile robots could be trained to learn information on collision-free paths and obstacle avoidance in an environment with several non-static dynamic obstacles. Most of this RL framework often integrates a path planner into the model to help mobile robots solve the difficulties of finding their path, which they may encounter in a difficult situation, especially in a dynamic obstacle environment. Reinforcement learning, however, is essential in understanding obstacle intent in a dynamic environment. Robots sometimes may plan their path without understanding the specific purpose of the obstacle, which may reduce mobile robot path planning efficiency. Likewise, interactions between obstacles are neglected, leading to avoidable trajectories. However, studies have demonstrated the importance of reinforcement learning in understanding obstacle intent. Evidence from a recent study conducted by [PERSON] et al. [37] affirmed through their experimental research that the mobile robot could learn collision-free policies through a deep reinforcement learning algorithm. Mobile robot obstacle interactions and obstacle-to-obstacle interactions can be properly understood using deep learning techniques of the reinforcement learning method. Meanwhile, previous scholars have suggested an angled pedestrian grid can help understand obstacle interaction in a dynamic environment. This is because it allows mobile robots to autonomously extract enough information about the dynamic environment, making them reach their goal without colliding with obstacles. Further, non-permanent obstacle characteristics can be adapted via an attention mechanism, thus allowing joint obstacle impact on the obstacle avoidance strategies.