Single-Player Monte-Carlo Tree Search
By Maarten P. D. Schadd, Mark H. M. Winands, H. Jaap Van Den Herik, Guillaume M, J-B Chaslot, Jos W. H. M. Uiterwijk. 2008
131 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Economics and Econometrics, Sociology and Political Science, Reinforcement Learning, Algorithms & Complexity
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
- Yuxi Li
- Pieter Abbeel
- Timothy P. Lillicrap
- Yoshua Bengio
- David Silver
- Jimmy Ba
- Ruslan Salakhutdinov
In Economics and Econometrics
- Yuxi Li
- Arthur Guez
- Timothy P. Lillicrap
- Thore Graepel
- Demis Hassabis
- Edward Jack Powley
- Cameron Browne
- Simon M. M. Lucas
In Sociology and Political Science
In Reinforcement Learning
- Sergey Levine
- Pieter Abbeel
- Timothy P. Lillicrap
- David Silver
- Yuxi Li
- Jimmy Ba
- Shimon Whiteson
- R´emi Munos
In Algorithms & Complexity
- David Silver
- Thore Graepel
- Eyke Hüllermeier
- Ioannis Antonoglou
- Arthur Guez
- Julian Schrittwieser
- Demis Hassabis
Citations in the corpus
- Mastering Atari, Go, chess and shogi by planning with a learned model (2019, methodology): “A simple solution would be to use the maximum score that can be observed in the environment to either re-scale the value or set the pUCT constants appropriately [33].”
- A Survey of Monte Carlo Tree Search Methods (2012, methodology): “This section describes some of the characteristics that make MCTS a popular choice of algorithm for a variety of domains, often with notable success.”
- ML-Plan: Automated machine learning via hierarchical planning (2018, background): “Modifications of UCT for single-player games that take this issue into account have been presented (Schadd et al. 2008; Bjornsson and Finnsson 2009).”