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

2021-05-11 · Damiano Brunori, Stefania Colonnese, Francesca Cuomo, Luca Iocchi

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.

📄 PDF Abstract BibTeX arXiv:2105.05094

Code (1)

DamianoBrunori/MultiUAV-OpenAIGym 공식 구현

Tasks

BenchmarkingEdge-computingOpenAI Gym reinforcement-learning Reinforcement Learning (RL)

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