Modification of improved upper confidence bounds for regulating exploration in Monte-Carlo tree search

DOI10.1016/J.TCS.2016.06.034zbMath1370.68263OpenAlexW2467873743MaRDI QIDQ307787

Yun-Ching Liu, Yoshimasa Tsuruoka

Publication date: 5 September 2016

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2016.06.034

zbMATH Keywords

multi-armed banditMonte Carlo tree searchupper confidence bounds

Mathematics Subject Classification ID

Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Combinatorial games (91A46) Probabilistic games; gambling (91A60)

Cites Work

• UCB revisited: improved regret bounds for the stochastic multi-armed bandit problem
• Asymptotically efficient adaptive allocation rules
• Simple Regret Optimization in Online Planning for Markov Decision Processes
• Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis
• Pure Exploration in Multi-armed Bandits Problems
• Finite-time analysis of the multiarmed bandit problem

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