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gptkbp:instanceOf
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gptkb:algorithm
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gptkbp:abbreviation
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gptkb:MCTS
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gptkbp:advantage
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asymptotic optimality
domain independence
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gptkbp:category
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gptkb:algorithm
gptkb:search_engine
reinforcement learning technique
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gptkbp:component
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gptkb:AlphaZero
gptkb:AlphaGo
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gptkbp:describedBy
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gptkb:Coulom,_2006,_'Efficient_Selectivity_and_Backup_Operators_in_Monte-Carlo_Tree_Search'
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gptkbp:field
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gptkb:artificial_intelligence
computer science
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gptkbp:hasComponent
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default policy
rollout
tree policy
value update
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gptkbp:hasVariant
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gptkb:RAVE
gptkb:Nested_MCTS
gptkb:PUCT
Parallel MCTS
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https://www.w3.org/2000/01/rdf-schema#label
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Monte Carlo Tree Search
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gptkbp:influenced
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modern AI game agents
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gptkbp:influencedBy
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gptkb:Monte_Carlo_method
gptkb:reinforcement_learning
minimax search
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gptkbp:introducedIn
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2006
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gptkbp:inventedBy
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gptkb:Rémi_Coulom
|
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gptkbp:limitation
|
high computational cost
difficulty with sparse rewards
|
|
gptkbp:notableFor
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gptkb:Go
gptkb:General_Game_Playing
Chess
|
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gptkbp:openSource
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gptkb:OpenAI_Gym
Python MCTS libraries
|
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gptkbp:relatedTo
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gptkb:Monte_Carlo_method
gptkb:UCT_algorithm
tree search
|
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gptkbp:step
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gptkb:simulation
backpropagation
expansion
selection
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gptkbp:usedIn
|
planning
robotics
optimization
game playing
|
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gptkbp:uses
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statistical analysis
random sampling
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gptkbp:bfsParent
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gptkb:game_AI
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gptkbp:bfsLayer
|
5
|