This paper considers the multi-armed bandit problem with multiple simultaneous arm pulls and the additional restriction that we do not allow recourse to arms that were pulled at some point in the past but then discarded. This additional restriction is highly desirable from an operational

Multi-armed bandit (MAB) problems are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative (competing) projects. Such problems are paradigms of a fundamental conflict between making decisions (allocating resources) that yield

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The following coins problem is a version of a multi-armed bandit problem where one has to select from among a set of objects, say classifiers, after an experimentation phase that is constrained by a time or cost budget. The question is how to spend the budget. The problem involves pure exploration

We consider a multi-armed bandit problem in a setting where each arm produces a noisy reward realization which depends on an observable random covariate. As opposed to the traditional static multi-armed bandit prob-lem, this setting allows for dynamically changing rewards that better describe

The stochastic multi-armed bandit problem is an important model for studying the exploration-exploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper

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We investigate a stochastic multi-armed bandit problem in which the forecaster’s choice is restricted. In this problem, rounds are divided into lock-up periods and the forecaster must select the same arm throughout a period. While there has been much work on finding optimal algorithms

We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of strategies that explore sequentially the arms. The strategies are assessed not in terms of their cumulative regrets, as is usually the case, but through quantities referred

The multi-armed bandit problem is a popular model for studying exploration/exploitation trade-off in sequential decision problems. Many algorithms are now available for this well-studied problem. One of the earliest algorithms, given by W. R. Thompson, dates back to 1933. This algorithm, referred