Deep policy iteration with integer programming for inventory management

Abstract

In this work, we discuss Programmable Actor Reinforcement Learning (PARL), a policy iteration method that uses techniques from integer programming and sample average approximation. We numerically benchmark the algorithm in complex supply chain settings where optimal solution is intractable and show its performs comparable to, and sometimes better than, state-of-the-art RL and commonly used inventory management benchmarks.