## 1 Introduction
Furthermore, it allows for direct handling of complex procedures, pre-trained behaviors allowing the robot to overcome new, unseen situations, making real-time decisions while interacting with the environment. The ability of a reinforcement learning agent to face unforeseen situations and respond to them is crucial in collaborative robotics [13], where adaptability to evolving scenarios, including unexpected factors such as human reactions, is critical. These key factors, such the ability to adapt to new situations and variables, highlight why reinforcement learning is one of the most appealing areas with respect to shaping the behavior of robots [14] in collaborative environments. In classical reinforcement learning, the learning process is conceptualized as a loop where an agent interacts with its environment in discrete time steps. Every step, the agent observes the current state of the environment and selects an action based on its policy--a strategy defining its behavior. The action, when executed by the agent in the specific environment, leads to a new state and provides a reward signal to the agent. The agent's goal is to learn a policy that maximizes the cumulative reward over time. This classical loop of observation, action, reward, and learning proposed by [PERSON] [15] (Figure 2)  
Figure 1: The various levels of cooperation between a human worker and a robot. continues until the agent achieves satisfactory performance or the environment changes significantly. In the realm of collaborative robotics training, RL plays a central role by enabling robots to learn from interactions within their environment. Typically, this training is conducted in virtual simulation environments, which allow for the execution of hundreds of interactions in a significantly reduced time frame compared to real-world trials. These simulations are instrumental in developing and refining the policies used to make decisions [16]. By utilizing RL, robots can optimize their actions to achieve specific goals, such as improving efficiency, accuracy, or safety in collaborative tasks. Numerous simulation platforms, such as Gymnasium [17] and ROS-Gazebo, are available to the scientific community [18]. These platforms offer diverse tools and frameworks that facilitate the development, testing, and validation of RL algorithms. They enable researchers to simulate complex scenarios, fine tune their models, and ensure that robotic systems can adapt to dynamic environments and interact effectively with human collaborators [12]. This controlled and accelerated training process enhances the reliability and efficiency of collaborative robotic systems before their deployment in real-world scenarios. Most approaches ignore the human aspect in training reinforcement learning agents when considering the two fields of collaborative robotics and reinforcement learning. As outlined in the opening of this article, factors like human well-being and workspace are significant variables. The approach of integrating reinforcement learning training with the human element via the use of EEG sensors to positively influence the collaborative robot's behavior has a lot of potential. The interpretation of EEG signals could be incorporated into the observation space (probably with a previous preprocessing phase to extract the relevant features), allowing the collaborative robot to perceive human states more accurately. This integration can significantly influence the reward function, enabling the robot to consider how the human operator is feeling while collaborating in the task. This review seeks to explore the different approaches when merging collaborative robotics, reinforcement learning, and the human element. It is important to note that quantifying human interaction, especially in terms of factors like trust in the robot or task satisfaction, can be challenging [19]. Numerous studies have develop new techniques and methods available for quantifying different variables with measurement devices such as EEG sensors.