Agile DQN: adaptive deep recurrent attention reinforcement learning for autonomous UAV obstacle avoidance

Agile DQN: adaptive deep recurrent attention reinforcement learning for autonomous UAV obstacle avoidance

Abstract Unmanned Aerial Vehicle (UAV) obstacle avoidance in 3D environments demands sophisticated handling of high-dimensional inputs and effective state representations. Current Deep Reinforcement Learning (DRL) algorithms struggle to prioritize salient aspects of state representations and manage...

Full description

Saved in:
Bibliographic Details
Main Authors: Fadi AlMahamid, Katarina Grolinger
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Autonomous unmanned aerial vehicles
Deep reinforcement learning
Autonomous visual navigation
Attention models
Deep learning
Deep Q-networks
Online Access:https://doi.org/10.1038/s41598-025-03287-y
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-03287-y

Similar Items