Neural Value Approximation
Neural Value Approximation is a value function approximation method in reinforcement learning that uses neural networks as the function approximator to estimate the value of states or state-action pairs, distinguishing itself from linear or table-based approximators by leveraging nonlinear representations for scalability and generalization in large or continuous state spaces.
5
Publications
694
Citations
20
Authors
9
Institutions
Publications per year
2017–2026
4
Parents
5
A distributional code for value in dopamine-based reinforcement learning
Will Dabney, Zeb Kurth‐Nelson, Naoshige Uchida et al. · Nature · 2020 · 476 citations · Full text
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
Bohan Zhuang, Chunhua Shen, Mingkui Tan et al. · 2019 · 168 citations
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Google DeepMind (United Kingdom)
London, United Kingdom
1 h-index
5 publications
2.4K citations
Oxford Centre for Computational Neuroscience
Oxford, United Kingdom
1 h-index
1 publications
476 citations
Prostate Cancer Research
London, United Kingdom
1 h-index
1 publications
476 citations

South China University of Technology
Guangzhou, China
1 h-index
2 publications
336 citations

Kobe University
Kobe, Japan
1 h-index
3 publications
48 citations

The University of Adelaide
Adelaide, Australia
1 h-index
5 publications
840 citations
Harvard University
Cambridge, United States
1 h-index
2 publications
952 citations
University College London
London, United Kingdom
1 h-index
2 publications
952 citations
1–8 of 9
Repository · 2 publications · 34 citations
2
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1 publications · 476 citations
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1–2