An Improved Sarsa( \lambda ) Reinforcement Learning Algorithm for Wireless Communication Systems

In this article, we provide a novel improved model-free temporal-difference control algorithm, namely, Expected Sarsa(λ), using the average value as an update target and introducing eligibility traces in wireless communication networks. In particular, we construct the update target using the average...

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Veröffentlicht in:	IEEE access Jg. 7; S. 115418 - 115427
Hauptverfasser:	Jiang, Hao, Gui, Renjie, Chen, Zhen, Wu, Liang, Dang, Jian, Zhou, Jie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Q learning Algorithms Communication networks Communications networks Control algorithms Control theory Decision making eligibility traces Machine learning Machine learning algorithms Markov processes Mathematical model Model-free reinforcement learning Numerical models Reinforcement learning Sarsa Wireless communication Wireless communication systems Wireless communications Wireless networks
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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