Space-indexed dynamic programming: learning to follow trajectories
By J. Kolter, Adam Coates, Andrew Y. Ng, Yi Gu, Charles DuHadway. 2008
21 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Jan Peters
- Pieter Abbeel
- Jens Kober
- J. Bagnell
- Chelsea Finn
- Gerhard Neumann
- Vladlen Koltun
In Computational Theory and Mathematics
- Sergey Levine
- J. Bagnell
- Jan Peters
- Jens Kober
- Pieter Abbeel
- J. Urgen Schmidhuber
- Gerhard Neumann
- Chelsea Finn
In Computer Vision and Pattern Recognition
- Jan Peters
- Jens Kober
- J. Bagnell
- Luc Van Gool
- Jitendra Malik
- Trevor Darrell
- Andrew Zisserman
- Andrea Vedaldi
Citations in the corpus
- Reinforcement learning in robotics: A survey (2013, background): “Kollar and Roy (2008) demonstrate the benefit of this reinforcement learning approach for developing state-of-the-art map exploration policies and Kolter et al. (2008) employed a space-indexed variant to learn trajectory following tasks with an autonomous vehicle and a RC car.”