On the Convergence Rate of MCTS for the Optimal Value Estimation in Markov Decision Processes

A recent theoretical analysis of a Monte-Carlo tree search (MCTS) method properly modified from the "upper confidence bound applied to trees" (UCT) algorithm established a surprising result, due to a great deal of empirical successes reported from heuristic usage of UCT with relevant adjus...

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Vydané v:	IEEE transactions on automatic control Ročník 70; číslo 7; s. 4788 - 4793
Hlavný autor:	Chang, Hyeong Soo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Approximation algorithms Artificial intelligence Complexity theory Computational modeling Convergence Data mining Estimation Markov decision process (MDP) Markov processes Monte-Carlo tree search (MCTS) multiarmed bandit (MAB) Search problems Training Uncertainty Upper bound upper confidence bound 1 (UCB1) upper confidence bound applied to trees (UCT)
ISSN:	0018-9286, 1558-2523
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