Action-advantage Function
The Action-Advantage Function is a value function in reinforcement learning that quantifies the relative benefit of selecting a specific action over a baseline policy by measuring the difference between the action-value function and the state-value function under a given policy.
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2017–2026
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Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel et al. · arXiv (Cornell University) · 2015 · 1.8K citations · Full text
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Air Force Engineering University
Xi'an, China
2 h-index
8 publications
196 citations

University of Bologna
Bologna, Italy
1 h-index
1 publications
16 citations
University of Illinois Urbana-Champaign
Urbana, United States
1 h-index
1 publications
23 citations
The University of Tokyo
Tokyo, Japan
1 h-index
2 publications
40 citations
California Institute of Technology
Pasadena, United States
1 h-index
3 publications
30 citations

Shanghai Jiao Tong University
Shanghai, China
1 h-index
1 publications
46 citations

City University of Macau
Macao, Macao
1 h-index
1 publications
76 citations

The University of Adelaide
Adelaide, Australia
1 h-index
3 publications
45 citations
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Repository · 6 publications · 1.9K citations
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1 publications · 21 citations
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1 publications · 19 citations
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Repository · 1 publications · 15 citations
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1 publications · 30 citations
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