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...

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Bibliographic Details

Published in:	Ocean engineering Vol. 321; p. 120402
Main Authors:	Sariman, Cagatay, Hallawa, Ahmed, Schmeink, Anke
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 30.03.2025
Subjects:	Autonomous underwater vehicles Morphological evolution Reinforcement learning Robotics Autonomous underwater vehicles Morphological evolution Robotics Reinforcement learning
ISSN:	0029-8018
Online Access:	Get full text
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