Hierarchical State Aggregation is a method in reinforcement learning that belongs to the class of state aggregation techniques, distinguished by its organization of states into a hierarchy to enable scalable value function approximation through multi-level abstraction.
15
Publications
1.8K
Citations
41
Authors
23
Institutions
Publications per year
2017–2026
3
Parents
Publications
15
Accurate medium-range global weather forecasting with 3D neural networks
Kaifeng Bi, Lingxi Xie, Hengheng Zhang et al. · Nature · 2023 · 1.4K citations · Full text
Distributed QoS routing with imprecise state information
Shigang Chen, Klara Nahrstedt · 2002 · 98 citations
Graying the black box: Understanding DQNs
Tom Zahavy, Nir Ben Zrihem, Shie Mannor · arXiv (Cornell University) · 2016 · 58 citations · Full text
1–5 of 15
Authors
41
- SM
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- SC
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- TZ
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- XC
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- KN
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1–5 of 41
Institutions
23
University of Notre Dame
Notre Dame, United States
1 h-index
1 publications
11 citations
Harbin Engineering University
Harbin, China
1 h-index
2 publications
46 citations
McGill University
Montreal, Canada
1 h-index
1 publications
20 citations
Motorola (United States)
Schaumburg, United States
1 h-index
1 publications
20 citations
State Key Laboratory of Polymer Physics and Chemistry
Beijing, China
1 h-index
4 publications
92 citations
Norwegian School of Economics
Bergen, Norway
1 h-index
1 publications
14 citations
Universität Greifswald
Greifswald, Germany
1 h-index
1 publications
14 citations
Xi'an Jiaotong University
Xi'an, China
1 h-index
1 publications
17 citations
1–8 of 23
Venues
- arXiv (Cornell University)
Repository · 3 publications · 97 citations
3
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- European Journal of Operational Research
2 publications · 47 citations
2
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- Applied Soft Computing
1 publications · 12 citations
1
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- Discrete Event Dynamic Systems
1 publications · 11 citations
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- IISE Transactions
1 publications · 17 citations
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