## 2 Background and Literature Review
The authors argued that the ultimate goal of RL is to provide robots with the abilities to learn, improve, adapt, and play in tasks with dynamically changing constraints based on navigation and self-learning. It was suggested that RL is appropriate for highly dynamic tasks with clear scales and argued that imitation learning should be easy to demonstrate, use clear practices, and be effective for slow work. The regression-based learning algorithm was effective when the goal was small. Further, the design of collaborative robots for assistance in assembly operations in manufacturing was investigated [10]. Collaborative robots are used in intelligent manufacturing-related environments  
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and are developed to learn from human demonstrations and support human partners in collaborative environments. According to the personal preferences of humans, natural language instructions can be used to teach robots. The robot learns from human demonstrations using the maximum entropy inverse RL algorithm, and the task-based learning is updated using the optimal assembly strategy. These studies have shown that RL can be effectively used in the design of human-robot collaboration. Regarding detailed investigation of how to imitate human behavior, studies have been conducted on methods of demonstrating an example motion for a robot in assembly work and extracting a manipulation function for robot learning and motion imitation [11]. In one method, the robot can directly learn how to control its movement. In a second method, when designing a robotic arm, a motion sensor can be attached to a human arm to enable human behavior to be mimicked. Finally, remote operation and control boxes can be utilized to provide hints to a robot. Each method was used to establish a strategic method by direct or indirect human intervention for robot learning. In addition to robot control, RL is applied and used effectively in various fields. For convenience in daily life, RL has been applied to drone delivery, home energy system optimization, autonomous driving, and automatic parking systems [12; 13; 14; 15]. In Internet of Things devices and networks, RL is mainly used to control traffic and congestion in complex situations. To reduce the collisions between the system and client effectively, the access method is designed using rule-based algorithms and RL. RL is also utilized as a means of selecting the appropriate channel [16; 17; 18; 19]. RL is applied to the problem of choosing a route to escape to a destination by avoiding obstacles. Several existing research studies handling human-centered RL technologies have been applied to various applications. [PERSON] and [PERSON] [20] and [PERSON] [21] applied RL techniques to several evacuation frameworks. In dynamic situations such as sudden obstacles or removals of exits, these frameworks generated evacuation routes considering humans' interactions and their congestions. Another application handling human-centered technologies and human-artificial hybrid intelligence is the bio-signal processing between human and a system with artificial intelligence modules. [PERSON] et al. [22] analyzed both human-system interactions using a stimulus-producing electroencephalogram (EEG). In the research study, EEG signals are obtained in real-time and are used for evaluating human's satisfactions with the interface which a system with artificial intelligence modules provides. Moreover, this area of research is directly related to drone control problems, where RL has been applied to design drones with obstacle avoidance. The data obtained from the sensor module mounted on the drone are used to configure the environment and state of the RL model, and the drone is controlled by designing an algorithm to maximize the reward value obtained from operation [12; 23]. RL is also used to design energy management systems to determine the balance between agents and optimal scheduling strategies.