2016/02/15 by Aurélien Garivier, Garivier, Aurélien, Emilie Kaufmann +3 · 2 citations
Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Artificial Intelligence in Games #Reinforcement Learning in Robotics
paper · pdf · doi:10.48550/arxiv.1602.04676
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.