# Reinforcement Learning for Autonomous Point-to-Point UAV Navigation

arXiv: https://arxiv.org/abs/2509.13943 · HTML: https://mlsanity.com/paper/2509.13943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 3 of 3)

- [Deep Reinforcement Learning-based UAV Navigation and Control: A Soft Actor-Critic with Hindsight Experience Replay Approach](https://mlsanity.com/paper/2106.01016/) (2021)
- [Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection](https://mlsanity.com/paper/2005.05057/) (2020)
- [Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment](https://mlsanity.com/paper/2504.05918/) (2025)

## Built on by (most cited first, 1 of 1)

- [No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation](https://mlsanity.com/paper/2607.19288/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) are increasingly used in automated inspection, delivery, and navigation tasks that require reliable autonomy. This project develops a reinforcement learning (RL) approach to enable a single UAV to autonomously navigate between predefined points without manual intervention. The drone learns navigation policies through trial-and-error interaction, using a custom reward function that encourages goal-reaching efficiency while penalizing collisions and unsafe behavior. The control system integrates ROS with a Gym-compatible training environment, enabling flexible deployment and testing. After training, the learned policy is deployed on a real UAV platform and evaluated under practical conditions. Results show that the UAV can successfully perform autonomous navigation with minimal human oversight, demonstrating the viability of RL-based control for point-to-point drone operations in real-world scenarios.