Data-Efficient Reinforcement Learning Framework for Autonomous Flight Based on Real-World Flight Data

Abstract

Recently, autonomous flight has emerged as a key technology in the aerospace and defense sectors; however, traditional code-based autonomous flight systems face limitations in complex environments. Although reinforcement learning offers an alternative, its practical application in real-world settings is hindered by the substantial data requirements. In this study, we develop a framework that integrates a Generative Adversarial Network (GAN) and Hindsight Experience Replay (HER) into model-based reinforcement learning to enhance data efficiency and accuracy. We compared the proposed framework against existing algorithms in actual quadcopter control. In the comparative experiment, we demonstrated an improvement of up to 70.59% in learning speed, clearly highlighting the impact of the environmental model. To the best of our knowledge, this study is the first where a GAN and HER are combined with model-based reinforcement learning, and it is expected to contribute significantly to the practical application of reinforcement learning in autonomous flight.