Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace:

Minimax lower bounds for the two-armed bandit problem
Kulkarni, Sanjeev R.; Lugosi, Gábor
Universitat Pompeu Fabra. Departament d'Economia i Empresa
We obtain minimax lower bounds on the regret for the classicaltwo--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.
bandit problem
minimax lower bounds
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
Documento de trabajo

Mostrar el registro completo del ítem