Multi-objective Q-learning
Multi-Objective Q-Learning is a reinforcement learning algorithm that extends the standard Q-learning framework to handle multiple conflicting objectives by learning a set of value functions or policies that approximate the Pareto optimal frontier in a multi-objective Markov decision process.
79
Publications
3.6K
Citations
306
Authors
142
Institutions
Publications per year
2017–2026
66
79
Optimization of Molecules via Deep Reinforcement Learning
Scientific Reports · 2019 · 390 citations · Full text
QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks
Jianmin Liu, Qi Wang, ChenTao He et al. · Computer Communications · 2019 · 164 citations · Full text
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142

South China University of Technology
Guangzhou, China
4 h-index
17 publications
821 citations
Politecnico di Milano
Milan, Italy
3 h-index
9 publications
246 citations
Beijing Jiaotong University
Beijing, China
3 h-index
11 publications
399 citations

University of York
York, United Kingdom
2 h-index
6 publications
216 citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
2 h-index
7 publications
473 citations

Hefei University of Technology
Hefei, China
2 h-index
11 publications
343 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
7 publications
1K citations
Liaocheng University
Liaocheng, China
2 h-index
5 publications
357 citations
1–8 of 142
7 publications · 309 citations
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4 publications · 267 citations
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4 publications · 147 citations
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3 publications · 54 citations
3
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3 publications · 80 citations
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