  Problem Definition: Are traditional deep reinforcement learning (DRL)
algorithms, developed for a broad range of purposes including game-play and
robotics, the most suitable machine learning algorithms for applications in
inventory control? To what extent would DRL algorithms tailored to the unique
characteristics of inventory control problems provide superior performance
compared to DRL and traditional benchmarks? Methodology/results: We propose and
study Deep Controlled Learning (DCL), a new DRL framework based on approximate
policy iteration specifically designed to tackle inventory problems.
Comparative evaluations reveal that DCL outperforms existing state-of-the-art
heuristics in lost sales inventory control, perishable inventory systems, and
inventory systems with random lead times, achieving lower average costs across
all test instances and maintaining an optimality gap of no more than 0.1\%.
Notably, the same hyperparameter set is utilized across all experiments,
underscoring the robustness and generalizability of the proposed method.
Managerial implications: These substantial performance and robustness
improvements pave the way for the effective application of tailored DRL
algorithms to inventory management problems, empowering decision-makers to
optimize stock levels, minimize costs, and enhance responsiveness across
various industries.
