Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments

In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 45; H. 3; S. 3476 - 3491
Hauptverfasser:	Bing, Zhenshan, Lerch, David, Huang, Kai, Knoll, Alois
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Agents (artificial intelligence) Algorithms Control tasks Decision making Deep learning Gaussian mixture model Inference algorithms Learning Machine learning Meta-reinforcement learning Multitasking Nonstationary environments Probabilistic models Representations Robot control robotic control Robots Skills Strategy task adaptation Task analysis task inference Training
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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