Hindsight-aware deep reinforcement learning algorithm for multi-agent systems

Classic reinforcement learning algorithms generate experiences by the agent's constant trial and error, which leads to a large number of failure experiences stored in the replay buffer. As a result, the agents can only learn through these low-quality experiences. In the case of multi-agent syst...

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Bibliographic Details
Published in:	International journal of machine learning and cybernetics Vol. 13; no. 7; pp. 2045 - 2057
Main Authors:	Li, Chengjing, Wang, Li, Huang, Zirong
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Buffers Communication Complex Systems Computational Intelligence Control Curricula Deep learning Efficiency Engineering Failure Machine learning Mechatronics Multiagent systems Original Article Pattern Recognition Robotics Success Systems Biology Teaching methods Multi-agent system Hindsight Experience replay Artificial intelligence Machine learning Reinforcement learning
ISSN:	1868-8071, 1868-808X
Online Access:	Get full text
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