A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run

scholarly article by A. Castelletti et al published June 2013 in Water Resources Research

A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run is …
instance of (P31):
scholarly articleQ13442814

External links are
P356DOI10.1002/WRCR.20295

P2093author name stringA. Castelletti
F. Pianosi
M. Restelli
P2860cites workObjective reduction in evolutionary multiobjective optimization: theory and applications.Q51833912
A fast and elitist multiobjective genetic algorithm: NSGA-IIQ56112663
Extremely randomized treesQ56221779
State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and ManagementQ57446772
Multiobjective Differential Evolution with Application to Reservoir System OptimizationQ57772385
Evolutionary multiobjective optimization in water resources: The past, present, and futureQ57818021
Assessing water reservoirs management and development in Northern VietnamQ59163922
P433issue6
P921main subjectreinforcement learningQ830687
P304page(s)3476-3486
P577publication date2013-06-01
P1433published inWater Resources ResearchQ7973358
P1476titleA multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run
P478volume49

Reverse relations

cites work (P2860)
Q59050415Intelligent Inventory Control via Ruminative Reinforcement Learning
Q57741180Making the most of data: An information selection and assessment framework to improve water systems operations
Q57741208Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management
Q57528613Multi-Objective Optimization for Analysis of Changing Trade-Offs in the Nepalese Water–Energy–Food Nexus with Hydropower Development

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