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Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple a...

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Detalles Bibliográficos
Autores principales: Orr, James, Dutta, Ayan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098527/
https://www.ncbi.nlm.nih.gov/pubmed/37050685
http://dx.doi.org/10.3390/s23073625
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author Orr, James
Dutta, Ayan
author_facet Orr, James
Dutta, Ayan
author_sort Orr, James
collection PubMed
description Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the environment not only learn from their own experiences but also from each other and its applications in multi-robot systems. In many real-world scenarios, one robot might not be enough to complete the given task on its own, and, therefore, we might need to deploy multiple robots who work together towards a common global objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi-robot systems are of tremendous significance from theoretical and applied standpoints, the latest survey in this domain dates to 2004 albeit for traditional learning applications as deep reinforcement learning was not invented. We classify the reviewed papers in our survey primarily based on their multi-robot applications. Our survey also discusses a few challenges that the current research in this domain faces and provides a potential list of future applications involving multi-robot systems that can benefit from advances in multi-agent deep reinforcement learning.
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spelling pubmed-100985272023-04-14 Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey Orr, James Dutta, Ayan Sensors (Basel) Review Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the environment not only learn from their own experiences but also from each other and its applications in multi-robot systems. In many real-world scenarios, one robot might not be enough to complete the given task on its own, and, therefore, we might need to deploy multiple robots who work together towards a common global objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi-robot systems are of tremendous significance from theoretical and applied standpoints, the latest survey in this domain dates to 2004 albeit for traditional learning applications as deep reinforcement learning was not invented. We classify the reviewed papers in our survey primarily based on their multi-robot applications. Our survey also discusses a few challenges that the current research in this domain faces and provides a potential list of future applications involving multi-robot systems that can benefit from advances in multi-agent deep reinforcement learning. MDPI 2023-03-30 /pmc/articles/PMC10098527/ /pubmed/37050685 http://dx.doi.org/10.3390/s23073625 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
Orr, James
Dutta, Ayan
Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
title Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
title_full Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
title_fullStr Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
title_full_unstemmed Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
title_short Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
title_sort multi-agent deep reinforcement learning for multi-robot applications: a survey
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098527/
https://www.ncbi.nlm.nih.gov/pubmed/37050685
http://dx.doi.org/10.3390/s23073625
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