# Here wedefineour bandits.Forthis example weareusinga four-armed bandit. The pullBanditfunctiongenerates a random numberfroma normal distributionwitha meanof0.The lower the bandit number, the more likely a positive reward will be returned. We want our agenttolearntoalways choose the bandit that...
For a Gaussian two-armed bandit, which arises when batch data processing is analyzed, the minimax risk limiting behavior is investigated as the control horizon N grows infinitely. The minimax risk is searched for as the Bayesian one computed with respect to the worst-case prior distribution. We...
41, No.6, 1906-1916 SOME REMARKS ON THE TWO-ARMED BANDIT1 BY J. FABIUS AND W. R. VAN ZWET University ofLeiden and M athematisch Centrum 1. Introduction and summary. In this paper we consider the following situation: An experimenter has to perform a total of N trials on two ...
We obtain minimax lower bounds on the regret for the classical two-armed bandit problem. We provide a finite-sample minimax version of the well-known log n asymptotic lower bound of Lai and Robbins (1985). Also, in contrast to the log n asymptotic results on the regret, we show that the...
摘要: Explicit formulae are obtained for the value and a stationary optimal policy in some cases of the continuous-time two-armed bandit problem with expected discounted reward.关键词: Two-armed bandit continuous time discounting optimization
We consider online learning in partial-monitoring games against an oblivious adversary. We show that when the number of actions available to the learner is two and the game is nontrivial then it is reducible to a bandit-like game and thus the minimax regret is $\Theta(\sqrt{T})$. Full-...
According to the main theorem of the theory of games, we search minimax strategy and minimax risk for the two-armed bandit problem as Bayes' ones corresponding to the worst prior distribution. Incomes are assumed to be normally distributed with unit variances and mathematical ex- pectations depend...
Suppose the arms of a two-armed bandit generate i.i.d. Bernoulli random variables with success probabilities ρ and λ respectively. It is desired to maximize the expected sum of N trials where N is fixed. If the prior distribution of (ρ, λ) is concentrated at two points (a, b) and...
ICSE'22 - Havoc-MAB: Enhancing AFL havoc mutation with Two-layer Multi-Armed Bandit - Tricker-z/havoc-mab
The aim is to maximize the expected number of successes in N trials by choosing one of the arms on each trial.doi:10.1007/978-3-642-45567-4_28Harald BenzingMichael KolonkoSpringer Berlin Heidelberg