Monte-Carlo Tree Search and Billiards - VideoLectures

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Baal: Infinite action spaceUCB is for finite action spacesHere, action space = R DProgressive widening: add instances as thenumber of simulation grows# instances ~ (# visits) ¼Coulom 07➢In a r<strong>and</strong>om order➢ In an educated orderAllows coupling with existing AL criteriasuch as VS split
Baal: draw surrogate hypothesesBilliard algorithmsRujan 97,Comets et. al. 09, ...Constraints = labeledinstancesPoint = hypothesisDomain = versionspaceSound: provably converges to uniform drawScalable w.r.t. dimension, # constraints
Page 1 and 2: European Conference in Machine Lear
Page 3 and 4: A major Machine Learning goalReduce
Page 5 and 6: How AL typically worksFind a way to
Page 7 and 8: Train the Active Learning PlayerIns
Page 9 and 10: UCT: Exploration Vs ExploitationUCB
Page 11: The Baal AlgorithmValue function fo
Page 15 and 16: Some results - 2Combining with ALcr
Page 17: Thanks for listening

Monte-Carlo Tree Search and Billiards - VideoLectures

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