Achieving Master Level Play in 9 x 9 Computer Go

By Sylvain Gelly, David Silver. 2008

58 citations recorded by the source corpus.

Subfields: Reinforcement Learning, Game & Decision Theory

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Citations in the corpus

Monte-Carlo tree search and rapid action value estimation in computer Go (2011, unknown): “Monte-Carlo tree search [1] is a new paradigm for search, which has revolutionised computer Go [2] [3], and is rapidly replacing traditional search algorithms as the method of choice in challenging domains such as General Game Playing [4], Amazons [5], Lines of Action [6], multi-player card games…”
Policy evaluation with temporal differences: a survey and comparison (2015, background): “Policy-iteration-based reinforcement learning has yielded impressive applications in robot soccer (Riedmiller and Gabel, 2007), elevator control (Crites and Barto, 1998) and game-playing such as Checkers (Samuel, 1959), Backgammon (Tesauro, 1994) and Go (Gelly and Silver, 2008).”
A Survey of Monte Carlo Tree Search Methods (2012, background): “Some authors take it to mean the complete sequence of actions chosen per iteration during both the tree and default policies (see for example [93], [204], [94]) while most take it to mean the sequence of actions chosen using the default policy only.”
Multi-armed bandits with episode context (2011, methodology): “Computer Go has been very challenging [5, 9], but major advances have been obtained using Monte Carlo techniques that evaluate positions using random playouts [7, 19, 20].”
Integrated task and motion planning in belief space (2013, background): “The process of constructing and evaluating a tree of this kind can be prohibitively expensive; but there have been recent advances in effective sample-based approaches (Gelly and Silver, 2008).”