2015 to \"Explora ciencia\" and \"Explora tecnologia\" projects call within the framework of the Spanish State Plan for Scientific and Technical Research and Innovation [8; 9]. The proposal for the pilot project was motivated by the need for completely efficient systems able to ensure the integrity of the protective clothing used by health providers in these challenging scenarios. In the face of the COVID-19 pandemic, healthcare systems worldwide have made every effort to ensure health care delivery and to scale up their systems and resources to prevent disease spreading [10]. The HORUS project seeks the development of robotic technologies to assess the integrity of PPE exposed to hazard infectious diseases. PPE is an important component of Infection Prevention and Control (IPC) measures. At the beginning of the aforementioned EHF outbreak, a lack of clear standards for PPE led to develop a rigorous advice guideline by the World Health Organization (WHO) [11]. Because safety concerns are especially important when donning, doffing and decontaminating the PPE, an actuation protocol on how to put on and how to remove it was provided [12]. These measures were primarily driven by the high potential for the protective suit to become contaminated with infected blood traces and body fluids after patient contact. Special care in the removal of the protective equipment as well as a visual inspection by the own health worker or by the instructor-observer (outside the changing room) are performed after an exposure [13]. Nevertheless, this human inspection results to be subjective and may be inaccurate. For these reasons, the HORUS project was focused towards developing a robotic system with an active vision system able to assess the PPE of health workers after contact with infected patients in an objective and effective way. A full-body scanner prototype which resembles the systems used in airports and offices was developed between 2016 and 2019 in the University Carlos III of Madrid. These scanners are designed to detect objects under clothing and not for its surface, so the underlying technology is expensive and generally makes use of X-rays or electromagnetic radiation with complex mechanisms [14]. The HORUS full-body scanner structure was implemented in a cost-efficient way using two motors over a linear plane guidance and a camera with motorized zoom and an illumination system (Figure 1). The choice of this kind of system requires the study and use of image processing algorithms able to provide reliable data for the detection of blood traces. The study of a set of computer vision algorithms was already tested in 2019 and is one of the research areas of the project [15]. Another promising research line brings together the use of an active vision system able to detect and classify defects in PPE and the latest trends in Artificial Intelligence (AI). The current software infrastructure allows to control the robotic system manually or set an automatic scan mode that stores a set of images in predefined positions into disk. A graphical user interface was developed by the members of the project team to provide specific options to set this manual or automatic mode, as well as the possibility to adjust certain image and camera properties. Figure 1: HORUS full-body scanner structure: (**left**) Overall mechanism view and (**right**) illumination system composed of a motorized camera DFK Z12G445 and 4 Neewer CN-Lux260 lamps. The main goal of this work is to explore and apply Reinforcement Learning (RL) to allow the current HORUS scanner platform to learn from interaction with its environment. This brings us closer to the possibility of training an AI agent for the specific blood detection task and turning it into a smarter operating system with increased reliability. The design of an agent-environment system has been simulated and evaluated considering many definitions and alternative forms of the elements of an RL problem.