Monte-Carlo Tree Search and Billiards - VideoLectures

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A different perspectiveActive learning can be seen as a gameplays x1answers h*(x1)plays x2answers h*(x2)Learner(with learningstrategy S). . .T-size training set S T(h*){(x1,h*(x1)), ... , (xT,h*(xT))}T= Finite HorizonTarget Concept h*(a.k.a. Oracle)Score: Generalization ErrorThis is a Reinforcement Learning problem
Train the Active Learning PlayerInspiration from GoCoulom 06, Chaslot et al. 06,Gelly&Sliver 07Explore the gametree: MCTSValues of movesestimated by <strong>Monte</strong>-<strong>Carlo</strong> simulationsAL: Train againstsurrogate hypotheses
Page 1 and 2: European Conference in Machine Lear
Page 3 and 4: A major Machine Learning goalReduce
Page 5: How AL typically worksFind a way to
Page 9 and 10: UCT: Exploration Vs ExploitationUCB
Page 11 and 12: The Baal AlgorithmValue function fo
Page 13 and 14: Baal: draw surrogate hypothesesBill
Page 15 and 16: Some results - 2Combining with ALcr
Page 17: Thanks for listening

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