Integrating Data Modeling and Dynamic Optimization using Constrained Reinforcement Learning

In this paper, we address the problem of tightly integrating data modeling and decision optimization, particularly when the optimization is dynamic and involves a sequence of decisions to be made over time. We propose a novel approach based on the framework of constrained Markov Decision Processes, and establish some basic properties concerning modeling/optimization methods within this formulation. We conduct systematic empirical evaluation of our approach on resource-constrained versions of business optimization problems using two real world data sets. In general, our experimental results exhibit steady convergent behavior of the proposed approach in multiple problem settings. They also demonstrate that the proposed approach compares favorably to alternative methods, which loosely couple data modeling and op-