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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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 23; H. 7; S. 3625
Hauptverfasser: Orr, James, Dutta, Ayan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 30.03.2023
MDPI
Schlagworte:
Algorithms
Deep learning
deep reinforcement learning
Expected utility
Expected values
multi-agent learning
multi-robot systems
Neural networks
Planning
Radio communications
Review
Robotics
Robots
survey
Surveys
Values
Massachusetts
Germany
multi-robot systems
survey
deep reinforcement learning
multi-agent learning
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s23073625