Amanote Research
Register
Sign In
Toward End-To-End Control for UAV Autonomous Landing via Deep Reinforcement Learning
doi 10.1109/icuas.2018.8453449
Full Text
Open PDF
Abstract
Available in
full text
Date
June 1, 2018
Authors
Riccardo Polvara
Massimiliano Patacchiola
Sanjay Sharma
Jian Wan
Andrew Manning
Robert Sutton
Angelo Cangelosi
Publisher
IEEE
Related search
End-To-End Driving in a Realistic Racing Game With Deep Reinforcement Learning
Reinforcement Learning-Based End-To-End Parking for Automatic Parking System
Sensors
Instrumentation
Information Systems
Electronic Engineering
Biochemistry
Analytical Chemistry
Molecular Physics,
Electrical
Atomic
Medicine
Optics
End-To-End Robotic Reinforcement Learning Without Reward Engineering
An End-To-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms
Sensors
Instrumentation
Information Systems
Electronic Engineering
Biochemistry
Analytical Chemistry
Molecular Physics,
Electrical
Atomic
Medicine
Optics
Exploring End-To-End Deep Learning Applications for Event Classification at CMS
EPJ Web of Conferences
Astronomy
Physics
LID-senone Extraction via Deep Neural Networks for End-To-End Language Identification
Deep-ACTINet: End-To-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy
Electronics (Switzerland)
Control
Electronic Engineering
Signal Processing
Computer Networks
Systems Engineering
Hardware
Communications
Electrical
Architecture
End-To-End Learning for Graph Decomposition
Deep fMRI: AN End-To-End Deep Network for Classification of fMRI Data