Controlled Markov Queueing Systems under Uncertainty

Alexander Mandel, Viktor Laptin
We investigate a model of a multilinear queueing system (QS) with channel switching under uncertainty, when statistical characteristics of the homogeneous Markov chain, which describes the transition probabilities of the environment from state to state, are unknown. Several reinforcement learning algorithms have been proposed to control such a system.