  We study regenerative stopping problems in which the system starts anew
whenever the controller decides to stop and the long-term average cost is to be
minimized. Traditional model-based solutions involve estimating the underlying
process from data and computing strategies for the estimated model. In this
paper, we compare such solutions to deep reinforcement learning and imitation
learning which involve learning a neural network policy from simulations. We
evaluate the different approaches on a real-world problem of shipping
consolidation in logistics and demonstrate that deep learning can be
effectively used to solve such problems.
