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Maximin Action Identification: A New Bandit Framework for Games

Abstract : We study an original problem of pure exploration in a strategic bandit model motivated by Monte Carlo Tree Search. It consists in identifying the best action in a game, when the player may sample random outcomes of sequentially chosen pairs of actions. We propose two strategies for the fixed-confidence setting: Maximin-LUCB, based on lower-and upper-confidence bounds; and Maximin-Racing, which operates by successively eliminating the sub-optimal actions. We discuss the sample complexity of both methods and compare their performance empirically. We sketch a lower bound analysis, and possible connections to an optimal algorithm.
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Contributor : Emilie Kaufmann Connect in order to contact the contributor
Submitted on : Monday, November 21, 2016 - 10:53:09 AM
Last modification on : Wednesday, October 27, 2021 - 1:00:13 PM
Long-term archiving on: : Tuesday, March 21, 2017 - 12:14:16 AM


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  • HAL Id : hal-01273842, version 2
  • ARXIV : 1602.04676


Aurélien Garivier, Emilie Kaufmann, Wouter Koolen. Maximin Action Identification: A New Bandit Framework for Games. 29th Annual Conference on Learning Theory (COLT), Jun 2016, New-York, United States. ⟨hal-01273842v2⟩



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