## 1 Introduction  
The fourth industrial revolution and digital innovation have led to the increased interest in the use of robots across industries; thus, robots are being used in various industrial sites. Robots can be broadly categorized as industrial or service robots. Industrial robots are automated equipment and machines used in industrial sites such as factory lines and are used for assembly, machining, inspection, and measurement as well as pressing and welding [1]. The use of service robots is expanding in manufacturing environments and other areas such as industrial sites, homes, and institutions. Service robots can be broadly divided into personal service robots and professional service robots. In particular, professional service robots are robots used by workers to perform tasks, and mobile robots are one of the professional service robots [2].  
A mobile robot needs the following elements to perform tasks: localization, mapping, and path planning. First, the location of the robot must be known in relation to the surrounding environment. Second, a map is needed to identify the environment and move accordingly. Third, path planning is necessary to find the best path to perform a given task [3]. Path planning has two types: point-to-point and complete coverage. A complete coverage path planning is performed when all positions must be checked, such as for a cleaning robot. A point-to-point path planning is performed for moving from the start position to the target position [4].  
To review the use of reinforcement learning as a path optimization technique for mobile robots in a warehouse environment, the following points must be understood. First, it is necessary to understand the warehouse environment and the movement of mobile robots. To do this, we must check the configuration and layout of the warehouse in which the mobile robot is used as well as the tasks and actions that the mobile robot performs in this environment. Second, it is necessary to understand the recommended research steps for the path planning and optimization of mobile robots. Third, it is necessary to understand the connections between layers and modules as well as their working when they are integrated with mobile robots, environments, hardware, and software. With this basic information, a simulation program for real experiments can be implemented. Because reinforcement learning can be tested only when a specific experimental environment is prepared in advance, substantial preparation of the environment is required early in the study but the researcher must prepare it. Subsequently, the reinforcement learning algorithm to be tested is integrated with the simulation environment for the experiment.  
This paper discusses an experiment performed using a reinforcement learning algorithm as a path optimization technique in a warehouse environment. Section 2 briefly describes the warehouse environment, the concept of path planning for mobile robots, the concept of reinforcement learning, and the algorithms to be tested. Section 3 briefly describes the structure of the mobile robot path-optimization system, the main logic of path search, and the idea of dynamically adjusting the reward value according to the moving action. Section 4 describes the construction of the simulation environment for the experiments, the parameters and reward values to be used for the experiments, and the experimental results. Section 5 presents the conclusions and directions for future research.