Tutorial on Bandits GamesThis tutorial was presented at ALT 2011. SlidesAbstractIn the recent years the multi-armed bandit problem has attracted a lot of attention in the theoretical learning community. This growing interest is a consequence of the large number of problems that can be modelized as a multi-armed bandit: web advertisement, dynamic pricing, online optimization, ect. Bandits algorithms are also used as building blocks in more complicated scenarios such as reinforcement learning, model selection problems, or games. While the basic stochastic multi-armed bandit can be traced back to Robbins (1952), it is only very recently that we obtained an (almost) complete understanding of this simple model. Moreover many extensions of the original problem have been proposed, such as bandits without a stochastic assumption (the so-called adversarial model), or bandits with a very large (structured) set of arms. The tutorial will be divided into three parts:
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