UR-EARL: A framework for designing underwater robots using evolutionary algorithm-driven reinforcement learning

Designing an autonomous underwater vehicle (AUV) is a significant challenge, which involves two primary tasks: shaping the body and developing the control system. Both elements are essential for the AUV’s effective performance. This study introduces a novel framework that integrates evolutionary alg...

Celý popis

Podrobná bibliografia

Vydané v:	Ocean engineering Ročník 321; s. 120402
Hlavní autori:	Sariman, Cagatay, Hallawa, Ahmed, Schmeink, Anke
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 30.03.2025
Predmet:	Autonomous underwater vehicles Morphological evolution Reinforcement learning Robotics Autonomous underwater vehicles Morphological evolution Robotics Reinforcement learning
ISSN:	0029-8018
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!