# Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2003.10923 · HTML: https://mlsanity.com/paper/2003.10923/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning](https://mlsanity.com/paper/2206.02670/) (2022)
- [Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2208.12328/) (2022)
- [A deep reinforcement learning approach to assess the low-altitude airspace capacity for urban air mobility](https://mlsanity.com/paper/2301.09758/) (2023)
- [Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review](https://mlsanity.com/paper/2510.21758/) (2025)
- [Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning](https://mlsanity.com/paper/2510.25679/) (2025)
- [Soft Deterministic Policy Gradient with Gaussian Smoothing](https://mlsanity.com/paper/2605.06228/) (2026)
- [Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN](https://mlsanity.com/paper/2606.24483/) (2026)
- [Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation](https://mlsanity.com/paper/2607.01794/) (2026)

## Abstract (arXiv)

In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.