MCTS

E748468

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.

All labels observed (1)

Label Occurrences
MCTS canonical 2

How this entity was disambiguated

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 ⓘ
goal approximate optimal decisions ⓘ
balance exploration and exploitation ⓘ
hasVariant Nested Monte Carlo Search ⓘ
Parallel MCTS ⓘ
Progressive Widening ⓘ
Rapid Action Value Estimation ⓘ
Upper Confidence bounds applied to Trees ⓘ
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 ⓘ
bandit algorithms ⓘ
reinforcement learning ⓘ
selectionPolicy upper confidence bounds for trees ⓘ
selectionPolicyAbbreviation UCT ⓘ
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 ⓘ

How these facts were elicited

Referenced by (2)

Full triples — surface form annotated when it differs from this entity's canonical label.