Stochastic optimization of multireservoir systems via reinforcement learning

Although several variants of stochastic dynamic programming have been applied to optimal operation of multireservoir systems, they have been plagued by a high‐dimensional state space and the inability to accurately incorporate the stochastic environment as characterized by temporally and spatially c...

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Veröffentlicht in:	Water resources research Jg. 43; H. 11
Hauptverfasser:	Lee, J.H, Labadie, J.W
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Blackwell Publishing Ltd 01.11.2007
Schlagworte:	case studies dynamic models dynamic programming Geum hydrologic models mathematical models Monte Carlo method optimization probabilistic models reinforcement learning reservoirs rivers spatial variation stochastic optimization stochastic processes temporal variation water flow South Korea Korea, Rep
ISSN:	0043-1397, 1944-7973
Online-Zugang:	Volltext
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