MCTS

E748468

Monte Carlo method heuristic search algorithm

MCTS is a heuristic search algorithm that uses randomized simulations to efficiently explore large decision trees, widely applied in game-playing AI and other complex planning problems.

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Statements (50)

Predicate	Object
instanceOf	Monte Carlo method ⓘ heuristic search algorithm ⓘ
advantage	can be interrupted at any time with best-so-far move ⓘ does not require evaluation function a priori ⓘ handles large branching factors ⓘ
canBeCombinedWith	domain knowledge ⓘ heuristic evaluation functions ⓘ neural networks ⓘ
coreStep	backpropagation ⓘ expansion ⓘ selection ⓘ simulation ⓘ
designedFor	large decision spaces ⓘ sequential decision-making problems ⓘ
fullName	Monte Carlo Tree Search NERFINISHED ⓘ
goal	approximate optimal decisions ⓘ balance exploration and exploitation ⓘ
hasVariant	Nested Monte Carlo Search NERFINISHED ⓘ Parallel MCTS NERFINISHED ⓘ Progressive Widening NERFINISHED ⓘ Rapid Action Value Estimation NERFINISHED ⓘ Upper Confidence bounds applied to Trees NERFINISHED ⓘ
input	game state ⓘ transition model or simulator ⓘ
limitation	high computational cost for deep search ⓘ performance depends on simulation quality ⓘ
operatesOn	decision trees ⓘ
output	action value estimates ⓘ recommended action ⓘ
property	anytime algorithm ⓘ asymmetric tree growth ⓘ does not require full tree expansion ⓘ model-free with respect to value function ⓘ
relatedTo	Markov decision processes NERFINISHED ⓘ bandit algorithms ⓘ reinforcement learning ⓘ
selectionPolicy	upper confidence bounds for trees ⓘ
selectionPolicyAbbreviation	UCT NERFINISHED ⓘ
uses	Monte Carlo rollouts ⓘ randomized simulations ⓘ statistical sampling ⓘ
widelyUsedIn	Go programs ⓘ chess engines ⓘ combinatorial optimization ⓘ game-playing artificial intelligence ⓘ general game playing ⓘ planning ⓘ real-time strategy games ⓘ robot motion planning ⓘ scheduling problems ⓘ

Referenced by (1)

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Monte Carlo tree search → abbreviation → MCTS ⓘ