  We design a multi-purpose environment for autonomous UAVs offering different
communication services in a variety of application contexts (e.g., wireless
mobile connectivity services, edge computing, data gathering). We develop the
environment, based on OpenAI Gym framework, in order to simulate different
characteristics of real operational environments and we adopt the Reinforcement
Learning to generate policies that maximize some desired performance.The
quality of the resulting policies are compared with a simple baseline to
evaluate the system and derive guidelines to adopt this technique in different
use cases. The main contribution of this paper is a flexible and extensible
OpenAI Gym environment, which allows to generate, evaluate, and compare
policies for autonomous multi-drone systems in multi-service applications. This
environment allows for comparative evaluation and benchmarking of different
approaches in a variety of application contexts.

Machine Learning;Multiagent Systems