Deep controlled learning of dynamic policies with an application to lost-sales inventory control

Recent literature established that neural networks can represent good policies across a range of stochastic dynamic models in supply chain and logistics. We propose a new algorithm that incorporates variance reduction techniques, to overcome limitations of algorithms typically employed in literature to learn such neural network policies. For the classical lost sales inventory model, the algorithm learns neural network policies that are vastly superior to those learned using model-free algorithms, while outperforming the best heuristic benchmarks by an order of magnitude. The algorithm is an interesting candidate to apply to other stochastic dynamic problems in supply chain and logistics, because the ideas in its development are generic.