Neural Value Function Approximation
Neural Value Function Approximation is a value function approximation method that employs artificial neural networks to model and generalize value estimates over states or state-action pairs, distinguishing it from tabular or linear function approximation approaches through its use of nonlinear, distributed representations.
33
Publications
1.3K
Citations
87
Authors
53
Institutions
Publications per year
2017–2026
16
Parents
33
Learning to Drive a Real Car in 20 Minutes
Martin Riedmiller, Mike Montemerlo, Hendrik Dahlkamp · 2007 · 92 citations
1–5 of 33
87
3
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 87
53
Colorado State University
Fort Collins, United States
2 h-index
2 publications
148 citations
Management Sciences (United States)
Albuquerque, United States
1 h-index
1 publications
12 citations

DEVCOM Army Research Laboratory
Adelphi, United States
1 h-index
2 publications
264 citations
Warsaw University of Technology
Warsaw, Poland
1 h-index
1 publications
27 citations

Supélec
Gif-sur-Yvette, France
1 h-index
2 publications
20 citations

Washington University in St. Louis
St Louis, United States
1 h-index
1 publications
64 citations
University of Washington
Seattle, United States
1 h-index
1 publications
64 citations
Texas A&M University at Qatar
Doha, Qatar
1 h-index
1 publications
96 citations
1–8 of 53
Repository · 3 publications · 47 citations
3
h-index
2 publications · 110 citations
2
h-index
2 publications · 58 citations
2
h-index
TFS · 1 publications · 26 citations
1
h-index
Repository · 1 publications · 12 citations
1
h-index
1–5