Fetching the paper…
Reading the bibliography…
Despite its groundbreaking success in Go and computer games, Monte Carlo Tree Search (MCTS) is computationally expensive as it requires a substantial number of rollouts to construct the search tree, which calls for effective parallelization.
Sub-gaussian random variables
Valerii V Buldygin and Yu V Kozachenko · 1980
Earlier work this paper cites.
Asymptotically efficient adaptive allocation rules
Tze Leung Lai and Herbert Robbins · 1985
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
Earlier work this paper cites.
Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
Earlier work this paper cites.
Exploration exploitation in go: Uct for monte-carlo go
Sylvain Gelly and Yizao Wang · 2006
Earlier work this paper cites.
Improved monte-carlo search
Levente Kocsis, Csaba Szepesvári, and Jan Willemson · 2006
Earlier work this paper cites.
On the parallelization of uct
Tristan Cazenave and Nicolas Jouandeau · 2007
Earlier work this paper cites.
Parallel monte-carlo tree search
Guillaume MJ-B Chaslot, Mark HM Winands, and H Jaap van Den Herik · 2008
Earlier work this paper cites.
The uct algorithm applied to games with imperfect information
Jan Schäfer, Michael Buro, and Knut Hartmann · 2008
Earlier work this paper cites.
Scalability and parallelization of monte-carlo tree search
Amine Bourki, Guillaume Chaslot, Matthieu Coulm, Vincent Danjean, Hassen Doghmen, Jean-Baptiste Hoock, Thomas Hérault, Arpad Rimmel, Fabien Teytaud, Olivier Teytaud, et al · 2010
Earlier work this paper cites.
Parallel monte-carlo tree search with simulation servers
Hideki Kato and Ikuo Takeuchi · 2010
Earlier work this paper cites.
Distributed learning in multi-armed bandit with multiple players
Keqin Liu and Qing Zhao · 2010
Earlier work this paper cites.
On the scalability of parallel uct
Richard B Segal · 2010
Cited alongside, same era.
Evaluating root parallelization in go
Yusuke Soejima, Akihiro Kishimoto, and Osamu Watanabe · 2010
Cited alongside, same era.
Determinization in monte-carlo tree search for the card game dou di zhu
Edward J Powley, Daniel Whitehouse, and Peter I Cowling · 2011
Cited alongside, same era.
Scalable distributed monte-carlo tree search
Kazuki Yoshizoe, Akihiro Kishimoto, Tomoyuki Kaneko, Haruhiro Yoshimoto, and Yutaka Ishikawa · 2011
Cited alongside, same era.
A survey of monte carlo tree search methods
Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
Cited alongside, same era.
Distributed exploration in multi-armed bandits
Eshcar Hillel, Zohar S Karnin, Tomer Koren, Ronny Lempel, and Oren Somekh · 2013
Cited alongside, same era.
Monte carlo tree search in go, 2017
Adrien Couëtoux, Martin Müller, and Olivier Teytaud · 2017
Later among the works it cites.
Monte-carlo tree search by best arm identification
Emilie Kaufmann and Wouter M Koolen · 2017
Later among the works it cites.
A multi-armed bandit approach for online expert selection in markov decision processes
Eric Mazumdar, Roy Dong, Vicenç Rúbies Royo, Claire Tomlin, and S Shankar Sastry · 2017
Later among the works it cites.
An analysis of virtual loss in parallel mcts
S Ali Mirsoleimani, Aske Plaat, H Jaap van den Herik, and Jos Vermaseren · 2017
Later among the works it cites.
Crushing candy crush: predicting human success rate in a mobile game using monte-carlo tree search, 2017
Erik Ragnar Poromaa · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep learning for real-time atari game play using offline monte-carlo tree search planning
Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L Lewis, and Xiaoshi Wang · 2014
Cited alongside, same era.
Minimizing simple and cumulative regret in monte-carlo tree search
Tom Pepels, Tristan Cazenave, Mark HM Winands, and Marc Lanctot · 2014
Cited alongside, same era.
Regulation of exploration for simple regret minimization in monte-carlo tree search
Yun-Ching Liu and Yoshimasa Tsuruoka · 2015
Cited alongside, same era.
Combining gameplay data with monte carlo tree search to emulate human play
Sam Devlin, Anastasija Anspoka, Nick Sephton, Peter I Cowling, and Jeff Rollason · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
On mabs and separation of concerns in monte-carlo planning for mdps
Zohar Feldman and Carmel Domshlak
Cited in the paper.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Later among the works it cites.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Later among the works it cites.
M-walk: Learning to walk over graphs using monte carlo tree search
Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, and Jianfeng Gao · 2018
Later among the works it cites.
Decentralized cooperative stochastic bandits
David Martínez-Rubio, Varun Kanade, and Patrick Rebeschini · 2019
Later among the works it cites.
Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2019
Later among the works it cites.
Watch the unobserved: A simple approach to parallelizing monte carlo tree search
Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, and Ji Liu · 2020
Closest in time.