## 2 Background and Literature Review  
This study applies reinforcement learning to the effective integration of human-machine evaluations toward a goal and compared previous studies on reinforcement learning on the basis of several categories. The learning object may be a robot, a system, or a game, and reinforcement learning is being investigated in various fields that require complex learning toward goals. There are a number of control strategies and relevant applications. [PERSON], et al. [1] proposed an adaptive control using fuzzy components to control a town crane. [PERSON], et al. [2] applied a semi-global state synchronization method to actuator control under unknown nonuniform input delay. When the learning object is a robot, these robots are generally classified as robot arms, humanoid robots, and industrial robots [3; 4; 5]. Robots with a high degree of freedom usually require complicated and difficult calculations because they require a goal that can perfectly mimic human behavior [6]. Additionally, robots learn how to behave similarly to humans so that they can also learn to expend a collaborative effort to help with human tasks or purposes [7]. The results from these studies can be applied to large-scale industrial applications that begin with learning and imitating simple movements, achieving goals through collaboration with humans, and applying them to real industries [8; 9]. Reinforcement learning is also often used in areas such as games, where the goal of learning is clearly stated. This is because the algorithm of reinforcement learning is rewarded and updated according to the actions taken by the learning object. For example, when the object performs a mission in the game, the reward it obtains depends on its actions, which affects the final result and the target value [10]. Studies have also been conducted to learn systems, besides robots and games. System network and communication are complex and computationally heavy because of the need for a system that can optimize performance and goals. Furthermore, the computational load of networks that optimize paths such as an escape is heavy because it is necessary to derive the learning results by computing problems in real time. Therefore, learning methods that effectively deal with the real-time computation of a problem have been investigated [11; 12; 13]. Moreover, various methods have been studied regarding the problem of reinforcement learning to deal with values of measuring devices such as sensors. There are methods for learning via effectively estimating, interpolating, and approximating values before preprocessing, such as in the case of sensor values [14; 15]. In addition, some studies have designed adaptive algorithms to ensure that learning objects are updated more reliably and effectively as they learn [16; 17; 18]. Table 1 summarizes the algorithms, contexts, and ideas proposed in previous studies and identifies the learning methods used in each study. These studies classified whether the learning object learned via the designed system itself or with human cooperation and also classified the learning objects into robots (robot arms and humanoids), games, or systems. In relation to the existing studies that have explored mimicking of human behavior, research related to the task of table lifting performed by humans and humanoid robots has been conducted. Humanoid robots move by predicting human movements through prediction-based algorithms, and the reliability of the prediction is arrived at by the motion predictor [19]. A study was conducted on dynamically walking and balancing robots that use reinforcement learning to learn dynamic gait without prior learning. It aimed to solve complex control problems with respect to the robot's motion control by mapping the motion space from discrete to continuous areas. The balanced learning method, which used the movement of the robot arms and legs to move the zero-moment point in the robot sole, can keep the biped robot in a static stable state.