Control of an Unmanned Surface Vehicle Based on Adaptive Dynamic Programming and Deep Reinforcement Learning

Autor:	Leonardo Garrido, Alejandro Gonzalez-Garcia, David Barragan-Alcantar, Ivana Collado-Gonzalez
Rok vydání:	2020
Předmět:	Dynamic programming Unmanned surface vehicle Artificial neural network Control theory Computer science Control (management) Reinforcement learning Control engineering Back propagation through time
Zdroj:	ICDLT
DOI:	10.1145/3417188.3417194
Popis:	This paper presents a low-level controller for an unmanned surface vehicle based on Adaptive Dynamic Programming (ADP) and deep reinforcement learning (DRL). The model-based algorithm Back-propagation Through Time and a simulation of the mathematical model of the vessel are implemented to train a deep neural network to drive the surge speed and yaw dynamics. The controller presents successful simulation results validating the feasibility of the proposed strategy and contributes to the diversity of validated applications of ADP and DRL control strategies.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e21812585fc59b26b5c4ff838b760014 https://doi.org/10.1145/3417188.3417194 Zobrazit plný text záznamu