## 5 Conclusions  
The efficiency of logistics stockers' operations was maximized by using RL in a factory simulation. Optimizing logistics automation is crucial for predicting and efficiently addressing various problems that arise in the production line. To optimize logistics automation, RL models simulate behaviors in various situations and continually adjust to determine the optimal outcome. The data necessary for logistics operations were analyzed, and the optimal course of action was determined. For example, to optimize logistics automation, RL models analyze the quantity, type, transfer, storage, and quality of products on a production line. Thus, the system collects data on the operation of a logistics automation robot and continuously adjusts it to perform logistics tasks optimally. The RL model identifies optimized routes to efficiently use the various resources required for logistics tasks. By optimizing the paths of logistics automation robots, the work time can be reduced, and problems on the production line can be prevented. By using RL in this manner, the diverse logistics tasks emerging on the production line can be managed, and production efficiency can be enhanced. The application of reinforcement learning resulted in an effectiveness of 30-100%, and optimizing logistics automation saves labor, time, and costs. It is clear that factory simulations can be effectively improved with reinforcement learning. Traditional rule-based simulators produce different results depending on the user's parameter settings, but AI-powered modeling can optimize multiple simulation outcomes, which is different from rule-based simulations that vary depending on the context. RL can be applied to factory simulations to identify optimal solutions for logistics automation equipment, which improves the efficiency of production operations. Intelligent logistics solutions can reduce the production lead time by calculating optimal routes through machine learning and zero human handling of products and data to promote unmanned factories. Factory simulation with reinforcement learning has seen many advances in recent years, and with these advances come challenges. Advances in data collection and storage technologies have resulted in rich datasets for factory simulation models. Advances in deep learning technology have enabled deep reinforcement learning (DRL) algorithms to improve the performance of reinforcement learning agents in complex environments. However, reinforcement learning requires a large number of samples, which is sometimes difficult to collect in a factory environment, and the data collected may be generalized to a specific environment. Designing the right reward function is a key issue in reinforcement learning, and setting the reward function correctly can be challenging, especially in complex factory simulation environments. Factory simulation environments can be time-varying and unstable, and reinforcement learning agents need to be able to respond to changes in this dynamic environment. Potential directions for future research include the development of more sophisticated reinforcement learning models and consideration of their application in a wider variety of logistics task scenarios, as well as improvements in data collection and analysis methods and examination of real-world applications. These studies suggest that the combination of factory simulation and reinforcement learning can further improve efficiency in the field of logistics automation. Conceptualization, J.-B.L. ; methodology, J.-B.L. ; software, J.-B.L. ; validation, J.-B.L. ; formal analysis, J.-B.L. ; investigation, J.-B.L. ; resources, J.-B.L. ; data curation, J.-B.L. ; writing--original draft preparation, J.-B.L. ; writing--review and editing, J.-B.L. ; visualization, J.-B.L. ; supervision, J.-B.L. ; project administration, J.J.; funding acquisition, J.-B.L. All authors have read and agreed to the published version of the manuscript.