Hypervolume indicator and dominance reward based multi-objective Monte-Carlo tree search
type: Item
instance of: scholarly article
title: Hypervolume indicator and dominance reward based multi-objective Monte-Carlo tree search
zbMATH Open document ID: 1273.68308
author: Michèle Sebag
author: Weijia Wang
published in: Machine Learning
publication date: 2013-10-22T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 65C05
zbMATH DE Number: 6217874
zbMATH Keywords: reinforcement learning
zbMATH Keywords: Monte-Carlo tree search
zbMATH Keywords: multi-objective optimization
zbMATH Keywords: sequential decision making
describes a project that uses: CMA-ES
describes a project that uses: SMS-EMOA
MaRDI profile type: Publication
full work available at URL: s10994-013-5369-0
OpenAlex ID: W1974819972
cites work: Finite-time analysis of the multiarmed bandit problem
cites work: Theory of the hypervolume indicator
cites work: SMS-EMOA: multiobjective selection based on dominated hypervolume
cites work: Markov Decision Processes with Multiple Long-Run Average Objectives
cites work: Multi-objective optimization using evolutionary algorithms
cites work: Q4424324
cites work: Q5405225
cites work: Q3093188
cites work: Algorithms for Reinforcement Learning
cites work: NP-complete scheduling problems
cites work: Workflow Scheduling Algorithms for Grid Computing
DOI: 10.1007/S10994-013-5369-0
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description: scientific article; zbMATH DE number 6217874