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.

A reinforcement learning environment for multi-service UAV-enabled wireless systems / Brunori, D.; Colonnese, S.; Cuomo, F.; Iocchi, L.. - (2021), pp. 251-256. (Intervento presentato al convegno 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021 tenutosi a online) [10.1109/PerComWorkshops51409.2021.9431048].

A reinforcement learning environment for multi-service UAV-enabled wireless systems

Brunori D.;Colonnese S.;Cuomo F.;Iocchi L.
2021

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 multi-drone systems in multi-service applications. This environment allows for comparative evaluation and benchmarking of different approaches in a variety of application contexts.
2021
2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021
multi-service UAV; reinforcement learning; UAV networks
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A reinforcement learning environment for multi-service UAV-enabled wireless systems / Brunori, D.; Colonnese, S.; Cuomo, F.; Iocchi, L.. - (2021), pp. 251-256. (Intervento presentato al convegno 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021 tenutosi a online) [10.1109/PerComWorkshops51409.2021.9431048].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1558488
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