In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has progressively increased thanks to advancements in multi-agent system technology, which enables the accomplishment of complex tasks that require cooperative and coordinated abilities. In this article, multi-UAV applications are grouped into five classes based on their primary task: coverage, adversarial search and game, computational offloading, communication, and target-driven navigation. By employing a systematic review approach, we select the most significant works that use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems and discuss their features using extensive and constructive critical reasoning. Finally, we present the most likely and promising research directions by highlighting the limitations of the currently held assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems. The suggested areas of research can enhance the transfer of knowledge from simulations to real-world environments and can increase the responsiveness and safety of UAV systems.

Unmanned aerial vehicles (UAVs) are supposed to be used to provide different services from video surveillance to communication facilities during critical and high-demanding scenarios. Augmented reality streaming services are especially demanding in terms of required throughput, computing resources at the user device, as well as user data collection for advanced applications, for example, location-based or interactive ones. This work is focused on the experimental utilization of a framework adopting reinforcement learning (RL) approaches to define the paths crossed by UAVs in delivering resources for augmented reality services. We develop an OpenAI Gym-based simulator that is tuned and tested to study the behavior of UAVs trained with RL to fly around a given area and serve augmented reality users. We provide abstractions for the environment, the UAVs, the users, and their requests. A reward function is then defined to encompass several quality-of-experience parameters. We train our agents and observe how they behave as a function of the number of UAVs and users at different hours of the day.

The spread of COVID-19 led to an extremely high number of deaths in Italy in 2020 with respect to previous years. Because the total number of deaths may depend on both the population structure and the mortality rate by age groups, a detailed overview of the Italian pandemic situation is here provided by following two main lines of inquiry: (i) checking for similarities and differences among mortality rates per age groups before and during the COVID-19 spread; (ii) analyzing the responsiveness of the Italian population structure to different mortality rates. Real-based evidence led us to conduct analyses for two groups associated with different population stages of life, referred to as younghood and adulthood periods. We focus on the Italian pandemic from February 2020 to March 2021. Our study helps to understand why elders dramatically impacted the total number of deaths. In addition, it reveals how badly the 2020 Italian population structure would have reacted to mortality rates already faced in the past. Finally, politicians, scientists, and journalists’ statements and other ways of communicating information about COVID-19 are questioned in the light of scientific data available at that time.