On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems

We investigate the optimality of perturbation based algorithms in the stochastic and adversarial multi-armed bandit problems. For the stochastic case, we provide a unified regret analysis for both sub-Weibull and bounded perturbations when rewards are sub-Gaussian... (read more)

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