  This paper describes the application of reinforcement learning (RL) to
multi-product inventory management in supply chains. The problem description
and solution are both adapted from a real-world business solution. The novelty
of this problem with respect to supply chain literature is (i) we consider
concurrent inventory management of a large number (50 to 1000) of products with
shared capacity, (ii) we consider a multi-node supply chain consisting of a
warehouse which supplies three stores, (iii) the warehouse, stores, and
transportation from warehouse to stores have finite capacities, (iv) warehouse
and store replenishment happen at different time scales and with realistic time
lags, and (v) demand for products at the stores is stochastic. We describe a
novel formulation in a multi-agent (hierarchical) reinforcement learning
framework that can be used for parallelised decision-making, and use the
advantage actor critic (A2C) algorithm with quantised action spaces to solve
the problem. Experiments show that the proposed approach is able to handle a
multi-objective reward comprised of maximising product sales and minimising
wastage of perishable products.
