Neural network and regression spline value function approximations for stochastic dynamic programming

Dynamic programming is a multi-stage optimization method that is applicable to many problems in engineering. A statistical perspective of value function approximation in high-dimensional, continuous-state stochastic dynamic programming (SDP) was first presented using orthogonal array (OA) experimental designs and multivariate adaptive regression splines (MARS). Given the popularity of artificial neural networks (ANNs) for high-dimensional modeling in engineering, this paper presents an implementation of ANNs as an alternative to MARS. Comparisons consider the differences in methodological objectives, computational complexity, model accuracy, and numerical SDP solutions. Two applications are presented: a nine-dimensional inventory forecasting problem and an eight-dimensional water reservoir problem. Both OAs and OA-based Latin hypercube experimental designs are explored, and OA space-filling quality is considered.