Adaptive dynamic programming and deep reinforcement learning for the control of an unmanned surface vehicle: Experimental results

This paper presents a low-level controller for an unmanned surface vehicle based on adaptive dynamic programming and deep reinforcement learning. This approach uses a single deep neural network capable of self-learning a policy, and driving the surge speed and yaw dynamics of a vessel. A simulation...

Full description

Bibliographic Details

Published in:	Control engineering practice Vol. 111; p. 104807
Main Authors:	Gonzalez-Garcia, Alejandro, Barragan-Alcantar, David, Collado-Gonzalez, Ivana, Garrido, Leonardo
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.06.2021
Subjects:	Adaptive dynamic programming Backpropagation through time Deep reinforcement learning Marine robotics Unmanned surface vehicle (USV) Backpropagation through time Deep reinforcement learning Unmanned surface vehicle (USV) Marine robotics Adaptive dynamic programming
ISSN:	0967-0661, 1873-6939
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!