# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Principles of Dynamic Obstacle Avoidance and Path Planning
In order to learn policies that would lessen the differences between the training and real-world driving settings, a soft actor-critic (SAC) reinforcement learning method was utilized. Driving to the goal point while maintaining forward and rotational velocity are all components of the reinforcement learning-based obstacle avoidance driving strategy. The goal of developing Mobile Robot Collision Avoidance Learning with Path (MCAL_P) was to address the issue of driving in a particular environment. It was integrated with a path planner, and the trained policy's input was changed with the help of lidar data, the robot's speed, and the separation from the look-ahead point. Through simulations and actual tests, it was demonstrated that the dynamic object avoidance performance was outstanding, and the obstacle avoidance performance was excellent. We confirmed that robots using MCAL_P can also avoid things that arise quickly at quick response rates in real-world environmental testing. We also discovered that because robots employing MCAL_P avoid one another according to the same rules, avoidance in multi-agent environments is more efficient and secure. However, several drawbacks also need to be addressed, such as the inability to drive straight, the inability to create efficient courses around static objects, and the lack of acceleration or deceleration when driving in addition to obstacle avoidance. Deep Reinforcement learning's application technique was explored by [PERSON], et al. [52] to aid the efficiency of rescue robots in urban search and rescue in an unknown clustered environment. The two most fundamental algorithms, Q-Learning and Dyna-Q, were put to the test, and the experimental outcomes were contrasted in terms of path search times and ultimate path lengths. Although the Q-Learning algorithm was found to be quicker, it struggled to locate search paths in a challenging setting with numerous barriers. With \"dynamic reward adjustment based on action to improve the results,\" the MR was improved. Robots on wheels typically seek areas with high reward potential. The experiment confirmed that a straightforward method to increase path search accuracy and keep path search duration could reduce ineffective exploration. The final path length was 71% less than the Q-learning algorithm, while the path search time was 5.5 times faster than the Dyna-Q methods. The warehouse environment in the field is significantly more complicated and constantly shifting, however. To tackle more complicated and realistic situations, a thorough analysis of the enhanced reinforcement learning algorithm is therefore required. MDRLAT, a brand-new multimodal DRL technique, was put forth in [53] for the obstacle avoidance of mobile indoor robots. The 2D laser range discoveries and depth photos were combined using the bilinear fusion (BF) module, and the resulting multimodal representation was supplied into the D3 QN to produce control commands for indoor mobile robots. The experimental outcomes demonstrated the method's exceptional performance in terms of the average accumulated reward, convergence speed, and success rate.