Paper #206

Título:

Minimax lower bounds for the two-armed bandit problem

Autores:

Sanjeev R. Kulkarni y Gábor Lugosi

Data:

Febrero 1997

Resumen:

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. Also, in contrast to the log $n$ asymptotic results on the regret, we show that the minimax regret is achieved by mere random guessing under fairly mild conditions on the set of allowable configurations of the two arms. That is, we show that for {\sl every} allocation rule and for {\sl every} $n$, there is a configuration such that the regret at time $n$ is at least 1 -- $\epsilon$ times the regret of random guessing, where $\epsilon$ is any small positive constant.

Palabras clave:

Bandit problem, minimax lower bounds

Códigos JEL:

C12, C73

Área de investigación:

Microeconomía

Publicado en:

IEEE Transactions on Automatic Control

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