Optimal Control of Semi-Markov Chains under Uncertainty with Applications to Queueing Models
Michael Kolonko, Manfred Schäl · 1980
In a semi-Markov decision model under uncertainty the law of motion depends on an unknown parameter θεθ. Conditions are given for the existence of a plan that is optimal, uniformly in θεθ, with respect to the average return criterion in a countable state model. The essential conditions are of Liapunov-type. The construction of the optimal plan follows an idea of Kurano and Mandl: at each step, choose an action that is optimal for an estimated value of the unknown parameter.