A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systems

Abstract

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 multidrone systems in multi-service applications. This environment allows for comparative evaluation and benchmarking of different approaches in a variety of application contexts.

Delivering Resources for Augmented Reality by UAVs: a Reinforcement Learning Approach

“…Our work starts from a multi-agent environment defined in Brunori et al (2021) and extends it with additional features, providing more complex and realistic implementation details. The major novelty of our work with respect to the other ones is the original model of the AR user demands, defined as a composition of requests for different resources and modeled as continuous functions over the timeline (24 h).…”