# NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images

arXiv: https://arxiv.org/abs/1807.08241 · HTML: https://mlsanity.com/paper/1807.08241/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Multi-Task Federated Reinforcement Learning with Adversaries](https://mlsanity.com/paper/2103.06473/) (2021)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones](https://mlsanity.com/paper/2304.03443/) (2023)
- [Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2208.12328/) (2022)
- [Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment](https://mlsanity.com/paper/2504.05918/) (2025)

## Abstract (arXiv)

We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable reward function is designed keeping in mind the cost and weight constraints for micro drone with minimum number of sensing modalities. Collection of small number of expert data and knowledge based data aggregation is integrated into the RL process to aid convergence. Experimentation is carried out on a Parrot AR drone in different indoor arenas and the results are compared with other baseline technologies. We demonstrate how the drone successfully avoids obstacles and navigates across different arenas.