Department of Computer, Control and Management Engineering

We provide a customizable environment based on Deep Reinforcement Learning (DRL) strategies for handling cooperative multi-UAV (Unmanned Aerial Vehicles) scenarios when delays are involved in the decision-making process for tasks such as spotting, tracking, coverage and many others. Users can choose among various combinations of tasks and parameters and customize the scenarios by implementing new desired functionalities. This environment provides the opportunity to compare different approaches, taking into account either implicitly or explicitly the delays applied to actions and observations. The awareness of the delay, along with the possible usage of real-world-based external files, increases the reality level of the environment by possibly easing the knowledge transferability process of the learned policy from the simulated environment to the real one. Finally, we show that use cases could generate new benchmarking tools for collaborative multi-UAV scenarios where DRL solutions must consider delays.