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We study regenerative stopping problems in which the system starts anewwhenever the controller decides to stop and the long-term average cost is to beminimized. Traditional model-based solutions involve estimating the underlyingprocess from data and computing strategies for the estimated model. In thispaper, we compare such solutions to deep reinforcement learning and imitationlearning which involve learning a neural network policy from simulations. Weevaluate the different approaches on a real-world problem of shippingconsolidation in logistics and demonstrate that deep learning can beeffectively used to solve such problems.

Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics