We study a two armed-bandit recursive algorithm with penalty. We show that the algorithm converges towards its ``target" although it always has a noiseless ``trap". Then, we elucidate the rate of convergence. For some choices of the parameters, we obtain a central limit theorem in which the limit distribution is characterized as the unique stationary distribution of a Markov process with jumps.
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Lamberton et al. (2008) studied this question.
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