An Asymptotically Optimal Strategy for Constrained Multi-armed Bandit Problems
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For the stochastic multi-armed bandit (MAB) problem from a constrained model that generalizes the classical one, we show that an asymptotic optimality is achievable by a simple strategy extended from the ϵ_t-greedy strategy. We provide a finite-time lower bound on the probability of correct selection of an optimal near-feasible arm that holds for all time steps. Under some conditions, the bound approaches one as time t goes to infinity. A particular example sequence of {ϵ_t} having the asymptotic convergence rate in the order of (1-1/t)^4 that holds from a sufficiently large t is also discussed.
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