  Gas Transmission Networks are large-scale complex systems, and corresponding
design and control problems are challenging. In this paper, we consider the
problem of control and management of these systems in crisis situations. We
present these networks by a hybrid systems framework that provides required
analysis models. Further, we discuss decision-making using computational
discrete and hybrid optimization methods. In particular, several reinforcement
learning methods are employed to explore decision space and achieve the best
policy in a specific crisis situation. Simulations are presented to illustrate
the efficiency of the method.
