Stochastic Optimal Control with Neural Networks and Application to a Retailer Inventory Problem
This document has been relocated to http://scholarsmine.mst.edu/mec_aereng_facwork/3479
There were 4 downloads as of 28 Jun 2016.
Overwhelming computational requirements of classical dynamic programming algorithms render them inapplicable to most practical stochastic problems. To overcome this problem a neural network based Dynamic Programming (DP) approach is described in this study. The cost function which is critical in a dynamic programming formulation is approximated by a neural network according to some designed weight-update rule based on Temporal Difference(TD)learning. A Lyapunov based theory is developed to guarantee an upper error bound between the output of the cost neural network and the true cost. We illustrate this approach through a retailer inventory problem.