  E-commerce with major online retailers is changing the way people consume.
The goal of increasing delivery speed while remaining cost-effective poses
significant new challenges for supply chains as they race to satisfy the
growing and fast-changing demand. In this paper, we consider a warehouse with a
Robotic Mobile Fulfillment System (RMFS), in which a fleet of robots stores and
retrieves shelves of items and brings them to human pickers. To adapt to
changing demand, uncertainty, and differentiated service (e.g., prime vs.
regular), one can dynamically modify the storage allocation of a shelf. The
objective is to define a dynamic storage policy to minimise the average cycle
time used by the robots to fulfil requests. We propose formulating this system
as a Partially Observable Markov Decision Process, and using a Deep Q-learning
agent from Reinforcement Learning, to learn an efficient real-time storage
policy that leverages repeated experiences and insightful forecasts using
simulations. Additionally, we develop a rollout strategy to enhance our method
by leveraging more information available at a given time step. Using
simulations to compare our method to traditional storage rules used in the
industry showed preliminary results up to 14\% better in terms of travelling
times.
