On nearly self-optimizing strategies for a discrete-time uniformly ergodic adaptive model

We control a discrete-time uniformly ergodic system, which depends on an unknown parameter α0 εA, a compact set. Our purpose is to minimize the long-run average-cost functional. We estimate the unknown parameter using the biased maximum likelihood estimator and apply the control which is almost optimal for the value of estimation. This way we construct strategies such that the value of the cost functional can be arbitrarily close to the optimal value obtained for α0.