## 1 Introduction  
Today's industry is advancing at a very fast pace, which is why there is a growing demand to integrate collaborative robots, often called cobots, into work environments. These robots are specifically designed to work alongside humans, sharing their workspace and even collaborating on various tasks [1]. However, the introduction of collaborative robots brings with it some new paradigms and problems that traditional industrial robots do not generate, such as issues related to unintentional human-robot collisions, balancing the workload distribution, and acceptance of collaborative robots, among others [2; 3]. That is why it becomes important to take into account aspects such as the work environment, human safety, and people's emotional well-being [4; 5]. In collaborative robotics, it is of great importance to consider the different levels of cooperation between a human worker and a robot. Figure 1[6] shows a very detailed representation of the different collaborative scenarios. A common industrial scenario is the cell model, where humans and robots are working in clearly differentiated compartments. The first collaborative model is coexistence, where humans and cage-free robots work alongside each other but do not share a workspace, maintaining separate areas of operation. The second model is synchronized interaction, where human workers and robots share a workspace but only one party is present in the workspace at any given time, ensuring a coordinated but separate workflow. The third model, cooperation, takes this a step further by allowing humans and robots to perform tasks simultaneously in the same workspace; however, they do not work concurrently on the same product or component. Finally, the most integrated model is collaboration, where the human worker and robot work in unison, simultaneously on the same product or component, exemplifying the highest level of partnership and synchronization in shared tasks. Emerging paradigms and problems discussed in the first paragraph that need to be taken into account have increased research efforts in the area of collaborative robotics, with the goal of understanding the impact of humans sharing most--if not all--tasks with robots. It is important to take into account aspects such as efficiency, trust, ergonomics (strain index [7]), and the emotional experiences of humans in environments shared with collaborative robots [8]. Addressing these challenges is key for developing a safe workspace [9]. Ensuring efficiency helps in optimizing task performance, trust is essential for smooth human-robot interaction, ergonomics prevents physical strain, and positive emotional experiences promote better collaboration and acceptance of robots in work environments [9; 10]. In the field of collaborative robotics, a leading idea revolves around the application of Reinforcement Learning (RL) to solve some of these challenges [11]. In the context of machine learning, reinforcement learning stands out as an approach in which an agent iteratively improves its behavior by interacting with its environment, receiving feedback through rewards and penalties to fine tune its decision-making process. Through these techniques, cobots can exhibit emergent behavior, and they can learn to handle simple tasks, which can be as basic as lifting a box or assembling a series of components. While it is true that through reinforcement learning, cobots can perform more difficult actions and exhibit more complex behaviors [12], these kinds of training tend to consume a great deal of time and computational resources. As mentioned in the previous paragraph when presenting reinforcement learning and its uses and strengths, the purpose of applying reinforcement learning to collaborative robotics is to optimize specific routines and potentially discover new behaviors that are otherwise unobtainable.