## 2 Background and Literature Review
It showed that the robot can learn how to improve motion in terms of the walking speed in the proposed way [20]. There have also been studies exploring flipping of handkerchiefs and folding of t-shirts. Emphasis was placed on the learning of the robotic arms, and deep reinforcement learning was investigated to learn complex policies through high-level observations such as typing. Because deep reinforcement learning requires a large number of training samples, a method that improves the sample efficiency and learning stability with fewer samples by combining the characteristics of smooth policy updates with automatic feature extraction of deep neural networks was proposed [21]. Similarly, there has been a study involving learning robots that pick up and classify objects. To be able to interact with dynamic objects in an unstructured environment, robots need manipulation capabilities to handle the confusion, change, and object variability. The robots learn a closed-loop policy that \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research Studies** & **Application** & **Learning Method** & **Human-Robot Interaction** & **System** \\ \hline \multirow{2}{*}{[PERSON] [19]} & Table-lifting task performed jointly by a human and robot & RL a & O c & Humanoid Robot \\ \hline \multirow{2}{*}{[PERSON] [20]} & The biped walking and balance control & RL & X d & Humanoid Robot \\ \hline \multirow{2}{*}{[PERSON] [21]} & Learning of cloth manipulation by dual-arm & DRL b & X & Robot Arm \\ \hline \multirow{2}{*}{[PERSON] [22]} & Object picking with a robotic manipulator & DRL & X & Robot Arm \\ \hline \multirow{2}{*}{[PERSON] [23]} & Collaborative robot to learn from human demonstrations about assembly tasks & RL & O & Robot Arm \\ \hline \multirow{2}{*}{[PERSON] [24]} & Door opening with a robotic manipulator & DRL & X & Robot Arm \\ \hline \multirow{2}{*}{[PERSON] [25]} & Training an agent manually evaluative reinforcement learning & RL & O & Game \\ \hline \multirow{2}{*}{[PERSON] [26]} & Learning continuous actions from corrective advice communicated by humans & RL & O & Game \\ \hline \multirow{2}{*}{[PERSON] [27]} & Adaptive human–machine real number evaluation in dynamic competing network & RL & O & Game \\ \hline \multirow{2}{*}{[PERSON] [28]} & Human crowd evacuation problem using look-ahead-based reinforcement learning & RL & X & System \\ \hline \multirow{2}{*}{[PERSON] [29]} & Complex agent-based model of evacuation & RL & X & System \\ \hline \multirow{2}{*}{[PERSON] [30]} & Block chain-empowered mobile edge computing & DRL & X & System \\ \hline \multirow{2}{*}{[PERSON] [31]} & Mobile edge computing IoT networks & DRL & X & System \\ \hline \end{tabular}  
* Reinforcement Learning (RL), b Deep Reinforcement Learning (DRL), c The manuscript handles human-robot interactions, d Human-robot interaction is not handled in the manuscript.  
\end{table} 
Table 1: Reinforcement Learning-Based Studies and Human–Robot Interaction. maps depth camera inputs to motion commands and compare different approaches to make the problem easier to deal with, including the reward formation, curriculum learning, and the use of pre-trained policies with reduced work to pre-start tasks. Training the robots with heuristics helped achieve the desired behavior [22]. Collaborative robots are widely used in hybrid assembly tasks involving intelligent manufacturing. Research related to teaching-learning-collaboration models, where collaborative robots can learn through human demonstrations and support human partners in the working environment, has been put forth. This approach allows humans to control the robot using natural language instructions according to their personal work preferences. The robot then learns the assembly demonstrations from human using the maximum entropy inverse reinforcement learning algorithm and updates the task-based knowledge with the optimal assembly strategy. In the collaborative process, the robot can leverage the learned knowledge to actively support people in collaborative assembly work.