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Refining the Confidence Level for Optimistic Bandit Strategies

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Figure

Figure 1: The regret of various algorithms as a function of ∆ when µ = (∆, 0, . . . , 0) and thenumber of arms is 2, 10 and 100 respectively
Table 2 outlines the long history of finite-armed stochastic bandits. It indicates which algorithmsare asymptotically optimal and/or sub-UCB and the ratio (up to constant factors) by which they areminimax suboptimal
Table 2: History of bandit algorithms
Table 3: Table of notation

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