Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach
Not on the map. Why?
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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 2; 2026: 3.
8 papers.
- 2022Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planningcited by 13
- 2022Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Reviewcited by 6
- 2023A deep reinforcement learning approach to assess the low-altitude airspace capacity for urban air mobilitycited by 1
- 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Reviewcited by 0
- 2025Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learningcited by 0
- 2026Soft Deterministic Policy Gradient with Gaussian Smoothingcited by 0
- 2026Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RANcited by 0
- 2026Lightweight Safe Reinforcement Learning for End-to-End UAV Navigationcited by 0
- 2026Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation2 Jul 2026
- 2026Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN23 Jun 2026
- 2026Soft Deterministic Policy Gradient with Gaussian Smoothing7 May 2026
- 2025Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning29 Oct 2025
- 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review11 Oct 2025
- 2023A deep reinforcement learning approach to assess the low-altitude airspace capacity for urban air mobility23 Jan 2023
- 2022Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review25 Aug 2022
- 2022Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning6 Jun 2022