  The note studies the problem of selecting a good enough subset out of a
finite number of alternatives under a fixed simulation budget. Our work aims to
maximize the posterior probability of correctly selecting a good subset. We
formulate the dynamic sampling decision as a stochastic control problem in a
Bayesian setting. In an approximate dynamic programming paradigm, we propose a
sequential sampling policy based on value function approximation. We analyze
the asymptotic property of the proposed sampling policy. Numerical experiments
demonstrate the efficiency of the proposed procedure.
