Using General Value Functions to Learn Domain-Backed Inventory
  Management Policies
Durgesh Kalwar Omkar Shelke Harshad Khadilkar
We consider the inventory management problem, where the goal is to balance
conflicting objectives such as availability and wastage of a large range of
products in a store. We propose a reinforcement learning (RL) approach that
utilises General Value Functions (GVFs) to derive domain-backed inventory
replenishment policies. The inventory replenishment decisions are modelled as a
sequential decision making problem, which is challenging due to uncertain
demand and the existence of aggregate (cross-product) constraints. In existing
literature, GVFs have primarily been used for auxiliary task learning. We use
this capability to train GVFs on domain-critical characteristics such as
prediction of stock-out probability and wastage quantity. Using this domain
expertise for more effective exploration, we train an RL agent to compute the
inventory replenishment quantities for a large range of products (up to 6000 in
the reported experiments), which share aggregate constraints such as the total
weight/volume per delivery. Additionally, we show that the GVF predictions can
be used to provide additional domain-backed insights into the decisions
proposed by the RL agent. Finally, since the environment dynamics are fully
transferred, the trained GVFs can be used for faster adaptation to vastly
different business objectives (for example, due to the start of a promotional
period or due to deployment in a new customer environment).