Online Multi-objective Learning
Online Multi-Objective Learning is a sequential decision-making framework within Multi-Objective Markov Decision Processes that dynamically optimizes multiple conflicting objectives in real-time as environmental feedback is incrementally acquired, distinguishing itself by its adaptive policy generation under non-stationary reward structures without prior knowledge of the objective space.
57
Publications
2.9K
Citations
218
Authors
106
Institutions
Publications per year
2017–2026
46
57
Empirical evaluation methods for multiobjective reinforcement learning algorithms
Peter Vamplew, Richard Dazeley, Adam Berry et al. · Machine Learning · 2010 · 274 citations · Full text
A reinforcement learning approach for dynamic multi-objective optimization
Fei Zou, Gary G. Yen, Lixin Tang et al. · Information Sciences · 2020 · 160 citations
1–5 of 57
218
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MTA-SZTE Research Group on Artificial Intelligence, Hungary
2 publications · 26 citations
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2
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1–5 of 218
106

Northeastern University
Shenyang, China
4 h-index
9 publications
698 citations

Oklahoma State University
Stillwater, United States
3 h-index
3 publications
264 citations
Wenzhou University
Wenzhou, China
2 h-index
2 publications
81 citations
Tsinghua University
Beijing, China
2 h-index
9 publications
329 citations
China University of Geosciences
Wuhan, China
2 h-index
7 publications
202 citations

Southwest Jiaotong University
Chengdu, China
2 h-index
6 publications
248 citations

Southeast University
Nanjing, China
2 h-index
2 publications
278 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
7 publications
438 citations
1–8 of 106
3 publications · 247 citations
3
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3 publications · 154 citations
3
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3 publications · 202 citations
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3 publications · 118 citations
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2 publications · 69 citations
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