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

arXiv: https://arxiv.org/abs/2105.05094 · HTML: https://mlsanity.com/paper/2105.05094/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 5 of 5)

- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [Unmanned Aerial Vehicle for Internet of Everything: Opportunities and Challenges](https://mlsanity.com/paper/2003.13311/) (2020)
- [UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design](https://mlsanity.com/paper/1802.03906/) (2018)
- [Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G](https://mlsanity.com/paper/2004.01920/) (2020)
- [A Survey on Cellular-connected UAVs: Design Challenges, Enabling 5G/B5G Innovations, and Experimental Advancements](https://mlsanity.com/paper/2005.00781/) (2020)

## Abstract (arXiv)

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.