A Reinforcement Learning Approach to Inventory Management

Abstract

This paper presents our approach for the control of a centralized distributed inventory management system using reinforcement learning (RL). We propose the application of policy-based reinforcement learning algorithms to tackle this problem in an effective manner. We have formulated the problem as a Markov decision process (MDP) and have created an environment that keeps track of multiple products across multiple warehouses returning a reward signal that directly corresponds to the total revenue across all warehouses at every time step. In this environment, we have applied various policy-based reinforcement learning algorithms such as Advantage Actor-Critic, Trust Region Policy Optimization and Proximal Policy Optimization to decide the amount of each product to be stocked in every warehouse. The performance of these algorithms in maximizing average revenue over time has been evaluated considering various statistical distributions from which we sample demand per time step per episode of training. We also compare these approaches to an existing approach involving a fixed replenishment scheme. In conclusion, we elaborate upon the results of our evaluation and the scope for future work on the topic.