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• W1821581985 abstract "Popular Monte-Carlo tree search (MCTS) algorithms for online planning, such as e-greedy tree search and UCT, aim at rapidly identifying a reasonably good action, but provide rather poor worst-case guarantees on performance improvement over time. In contrast, a recently introduced MCTS algorithm BRUE guarantees exponential-rate improvement over time, yet it is not geared towards identifying reasonably good choices right at the go. We take a stand on the individual strengths of these two classes of algorithms, and show how they can be effectively connected. We then rationalize a principle of selective tree expansion, and suggest a concrete implementation of this principle within MCTS. The resulting algorithms favorably compete with other MCTS algorithms under short planning times, while preserving the attractive convergence properties of BRUE." @default.
• W1821581985 created "2016-06-24" @default.
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• W1821581985 date "2013-08-11" @default.
• W1821581985 modified "2023-09-26" @default.
• W1821581985 title "Monte-Carlo planning: theoretically fast convergence meets practical efficiency" @default.
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