## 1 Introduction  
The most potent and influential technology of 21 st century is Wireless Sensor Network (WSN). This is a key technology in the ubiquitous network, and is becoming more important as sensors can connect everything to the Internet and process important information. It also collects information about the surrounding environment (temperature, humidity, etc.) with a specific purpose and transmits it to the base station through various processes. With the advancements of WSN disposable sensors shows enhanced performance on the fields where the user cannot access or include large area, such as forest or deep sea [1]. In addition, the size of the sensor node should be small as it includes batteries, making it impossible to charge or replace, and often forming a wireless network by deploying a large number of sensors. And because it uses a battery, it cannot be charged or replaced. In addition, the size of the sensor node must be small as a large number of sensors are placed to form a wireless network. In addition, the price should be low, so there is a limit to the ability to process data and the amount of power that can be supplied to the nodes [2]. In general, in a wireless network environment, disposable sensor nodes that make up the network must perform routing and sensing roles together, so each sensor node always has an energy burden [3]. Due to these characteristics of disposable sensor network, the efficient use of limited power is of paramount importance in WSN design. Thus, various studies are currently being conducted to maximize energy consumption efficiency and to increase the overall life span. Since routing protocols in WSN are very important to efficiently transmit detected data to the Base Station (BS), clustering-based routing is preferred to obtain the advantage of efficient communication. As a common low-energy cluster-based protocol, Low-Energy Adaptive Clustering Hierarchy (LEACH) was proposed [4]. This is a technique that utilizes random rotation of BS to distribute network sensors and energy loads evenly. Only Cluster Heads (CH) interact with BS, which saves energy and increases network lifetime [5]. Thus, the idea of clustering-based routing is aimed at saving energy on sensor nodes by using CH's information aggregation mechanism to reduce the amount of data transmission and energy. Thus, in the case of LEACH, the advantage of ensuring even energy use is achieved by using the probability function, but all nodes should take part in the CH election process every round, and, if a few CHs are elected, the number of nodes to be managed increases, resulting in high network traffic and increased energy consumption. Therefore, various LEACHs were developed to compensate for these problems in the preceding studies. In recent years, extensive research has been conducted on WSN's various systems of artificial intelligence approaches, called Reinforcement Learning (RL), to improve network performance, and there is much interest in applying them [6]. A Q-learning-based cooperative sensing plan is proposed to enhance the performance of spectrum in a wireless network environment [7]. In Autonomous Things environment, the interests on disposable sensors with characteristics that can be easily sprayed and moved is emerging. Therefore, efficient energy management is required considering these topology changes, in real life. As limits exist for applying multiple LEACH algorithm with fixed topology state in previous studies, the development of a new dynamic topology model is required. In addition, as opposed to traditional LEACH and D-LEACH, which performs LEACH in its jurisdiction by dividing the zones, D-LEACH is efficient in the static topology simulation; however, in dynamic topology simulation, with higher uncertainty, the advantage of D-LEACH is not expected.