# Autonomous Quadrotor Landing using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1709.03339 · HTML: https://mlsanity.com/paper/1709.03339/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 4 of 4)

- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (2023)
- [(Ir)rationality in AI: State of the Art, Research Challenges and Open Questions](https://mlsanity.com/paper/2311.17165/) (2023)
- [Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing](https://mlsanity.com/paper/2505.06963/) (2025)
- [Reinforcement Learning for Robotic Safe Control with Force Sensing](https://mlsanity.com/paper/2512.02022/) (2025)

## Abstract (arXiv)

Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of hand-crafted geometric features and the use of external sensors in order to allow the vehicle to approach the land-pad. In this article, we propose a method based on deep reinforcement learning that only requires low-resolution images taken from a down-looking camera in order to identify the position of the marker and land the UAV on it. The proposed approach is based on a hierarchy of Deep Q-Networks (DQNs) used as high-level control policy for the navigation toward the marker. We implemented different technical solutions, such as the combination of vanilla and double DQNs, and a partitioned buffer replay. Using domain randomization we trained the vehicle on uniform textures and we tested it on a large variety of simulated and real-world environments. The overall performance is comparable with a state-of-the-art algorithm and human pilots.