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A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin...

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Detalles Bibliográficos
Autores principales: Han, Dong, Mulyana, Beni, Stankovic, Vladimir, Cheng, Samuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098871/
https://www.ncbi.nlm.nih.gov/pubmed/37050822
http://dx.doi.org/10.3390/s23073762
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author Han, Dong
Mulyana, Beni
Stankovic, Vladimir
Cheng, Samuel
author_facet Han, Dong
Mulyana, Beni
Stankovic, Vladimir
Cheng, Samuel
author_sort Han, Dong
collection PubMed
description Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.
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spelling pubmed-100988712023-04-14 A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation Han, Dong Mulyana, Beni Stankovic, Vladimir Cheng, Samuel Sensors (Basel) Review Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject. MDPI 2023-04-05 /pmc/articles/PMC10098871/ /pubmed/37050822 http://dx.doi.org/10.3390/s23073762 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Han, Dong
Mulyana, Beni
Stankovic, Vladimir
Cheng, Samuel
A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
title A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
title_full A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
title_fullStr A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
title_full_unstemmed A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
title_short A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
title_sort survey on deep reinforcement learning algorithms for robotic manipulation
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098871/
https://www.ncbi.nlm.nih.gov/pubmed/37050822
http://dx.doi.org/10.3390/s23073762
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