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Optimal policy pulls arm 1 until some stopping time ˝, arm 2 forever after that if ˝<1. The top solutions are determined by popularity, ratings and frequency of searches. the operating lever looks like an arm, and the machine in effect robs players, since it "wins" and keeps the player's money in an overwhelming majority of instances. . 1 Introduction The 2-arm bandit problem is the classical example of the exploration vs. In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K-or N-armed bandit problem) is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known at. Affordable and search from millions of royalty free images, photos and vectors. A real nice example of the $1 Mills hightop "Special Award" slot with a new stand in oak. A muti-armed bandit, on the other hand, is more. 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