The Best of Both Worlds: Stochastic and Adversarial Bandits

Abstract

We present a new bandit algorithm, SAO (Stochastic and Adversarial Optimal) whose regret is (essentially) optimal both for adversarial rewards and for stochastic rewards. Specifically, SAO combines the O( √ n) worst-case regret of Exp3 (Auer et al., 2002b) and the (poly)logarithmic regret of UCB1 (Auer et al., 2002a) for stochastic rewards. Adversarial… (More)

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