A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systems
This provides a tool for researchers and practitioners in UAV-enabled wireless systems to benchmark reinforcement learning approaches, but it is incremental as it builds on existing frameworks.
The paper tackles the problem of simulating autonomous UAVs offering multiple communication services by designing a flexible OpenAI Gym environment, and results show it enables policy generation and comparison with a baseline for evaluation.
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.