## 1 Introduction  
In order to operate a mobile robot in an automated warehouse, the following realistic conditions must be considered. First, it is necessary to solve the Multi-Agent Path Finding (MAPF) problem, in which multiple mobile robots work simultaneously in an automated warehouse to find the optimal path for given tasks [1]. Second, we have to deal with the Multi-Agent Pickup and Delivery (MAPD) issue [1]. The actual operation of the mobile robots is not simply departure and arrival. In automated warehouses, mobile robots perform a more complex and sequential series of actions to deliver goods to a designated location.  
Reinforcement learning algorithms are widely used to solve various decision making problems in complex environments. Recently, the demand for multi-agent environments has been rapidly increasing, as is interest in Multi-Agent Reinforcement Learning (MARL) algorithms [2]. In general, MARL aims to simultaneously train multiple agents to perform a given task in a shared environment [3]. In this paper, we use a Multi-Agent Reinforcement Learning (MARL) algorithm to handle additional conditions for mobile robots in a warehouse.  
We simulate the environment of an automated warehouse and used a MARL algorithm to simulate multiple mobile robots. The movement of a mobile robot is not a simple operation; it is complex. We are also trying to increase the usability of training by allowing mobile robots to learn from a variety of positions rather than from a fixed position.  
Reinforcement learning can proceed efficiently when the agent is smoothly rewarded for the actions performed. However, in some reinforcement learning experiments, there is no learning because of sparse rewards. In this paper, we experimentally verify that multiple mobile robots can perform realistic operations in an automated warehouse. In particular, we propose a method to improve the sparse reward problem of the learning path with multiple mobile robots in automated warehouses and named it the Dual Segmented Reward Model.  
The Dual Segmented Reward Model method expects contributions to the following items:  
1. A reward model is proposed so that learning can proceed efficiently and stably in an environment where the sparse reward problem of reinforcement learning has become serious.
2. The proposed reward model induces and supports reinforcement learning efficiently and stably by using the reward model without modifying the reinforcement learning algorithm or changing the environment.
3. It is meaningful as a practical case study that confirms learning efficiently and stably despite the sparse reward problem in a simulation experiment environment similar to an actual automated warehouse.  
The paper consists of the following contents. Section 2 reviews the types of reinforcement learning algorithms and briefly reviews the meaning of Model-Free, Model-Based, Value-Based, and Policy-Based. For Multi-Agent Reinforcement Learning (MARL), we review the differences between single agents and multiple agents, their algorithms, types based on agent relationship, and types of learning and execution. The algorithms to be used in the experiment are reviewed, as well. We also briefly review methods to ameliorate the sparse reward problem. In Section 3, we explain the idea and definition of the proposed \"Dual Segmented Reward Model.\" Section 4 describes the simulation environment, experimental methods and cases, detailed modeling information of the proposed method, the parameters to be used for the experiment, and the experimental results. Section 5 presents general conclusions and opinions, as well as directions for future research.